Migrated from GitHub

.gitattributes

@@ -58,3 +58,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/HomoAug/bin/TMalign filter=lfs diff=lfs merge=lfs -text
+data/HomoAug/bin/esl-reformat filter=lfs diff=lfs merge=lfs -text
+data/HomoAug/bin/jackhmmer filter=lfs diff=lfs merge=lfs -text

data/HomoAug/LICENSE.txt

@@ -0,0 +1,216 @@
+Copyright © 2023 Institute for AI Industry Research (AIR), Tsinghua University.
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data/HomoAug/README.md

@@ -0,0 +1,199 @@
+# Code for HomoAug
+
+Homoaug is a data augmentation method for generate pseudo-pocket-ligand pairs. It is based on the idea that the ligand binding sites of homologous proteins are similar. The details of HomoAug can be found in our paper: "DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening"
+
+## Requirements
+
+To run the code, you need to install the following packages:
+
+- ray (version 1.12.0)
+- jackmmer (a part of HMMER)
+- esl-reformat (a part of HMMER)
+- TM-align 
+
+Please use 'pip install ray==1.12.0' to install ray.
+
+The exeauctable files of jackmmer, esl-reformat and TM-align are included in our folder 'bin', please add the path of 'bin' to your environment variable.
+
+## Usage
+
+<!---     parser = argparse.ArgumentParser()
+    parser.add_argument("--id_file", type=str, default="/drug/BioLip/tmp.id")
+    parser.add_argument("--homoaug_dir", type=str, default="/drug/BioLip/homoaug_new")
+    parser.add_argument(
+        "--fasta_file",
+        type=str,
+        default="/drug/BioLip/BioLiP_v2023-04-13_regularLigand.fasta",
+    )
+    parser.add_argument(
+        "--protein_pdb_dir", type=str, default="/drug/BioLip/protein_pdb"
+    )
+    parser.add_argument(
+        "--pocket_pdbs_dir", type=str, default="/drug/BioLip/pocket_pdb"
+    )
+    parser.add_argument(
+        "--jackhmmer_output_dir", type=str, default="/drug/BioLip/pdbbind_MSA_fasta"
+    )
+    parser.add_argument(
+        "--n_thread", type=int, default=10, help="number of threads for running"
+    )
+    parser.add_argument(
+        "--database_fasta_path",
+        type=str,
+        default="/data/protein/AF2DB/AFDB_HC_50.fa",
+        help="jackhmmer search database, in fasta format",
+    )
+    parser.add_argument(
+        "--database_pdb_dir",
+        type=str,
+        default="/drug/AFDB_HC_50_PDB",
+        help="homoaug search database, e.g. AF2DB",
+    )
+    parser.add_argument(
+        "--max_extend_num",
+        type=int,
+        default=20,
+        help="max number of extended pocket-ligand pairs for one real pocket-ligand pair",
+    )
+    parser.add_argument(
+        "--TMscore_threshold",
+        type=float,
+        default=0.4,
+        help="TMscore threshold for extending",
+    )
+    parser.add_argument(
+        "--Match_rate_threshold",
+        type=float,
+        default=0.4,
+        help="Match_rate threshold for extending",
+    )
+-->
+
+
+To use HomoAug, you only need to run the following command:
+
+```bash
+    python run_HomoAug.py
+        --id_file your_id_file
+        --homoaug_dir your_homoaug_dir
+        --fasta_file your_fasta_file
+        --protein_pdb_dir your_protein_pdb_dir
+        --pocket_pdbs_dir your_pocket_pdbs_dir
+        --jackhmmer_output_dir your_jackhmmer_output_dir
+        --n_thread your_n_thread
+        --database_fasta_path your_database_fasta_path
+        --database_pdb_dir your_database_pdb_dir
+        --max_extend_num your_max_extend_num
+        --TMscore_threshold your_TMscore_threshold
+        --Match_rate_threshold your_Match_rate_threshold
+```
+
+We will explain the meaning of each parameter in the following.
+
+
+#### --id_file
+
+The id_file is a file containing the ids of the real pocket-ligand pairs. 
+
+For example, the id_file of BioLip dataset can be like this:
+
+```
+2WNS_B_receptor_B_550_OMP
+5RVK_A_receptor_A_201_2AK
+5F03_A_receptor_A_301_5TA
+7EXF_B_receptor_B_801_GAL
+4OJ4_A_receptor_A_501_DIF
+1PJ7_A_receptor_A_2887_FFO
+......
+```
+
+You need to create your own id_file according to your dataset, with the format like :`<pdb_id>_<chain_id>_<any_string>_<ligand_name_in_pdb>
+
+#### --homoaug_dir
+
+The output directory of HomoAug. When the program is finished, you will find the extended pocket-ligand pairs in this directory.
+
+The structure of homoaug_dir is like this:
+
+```
+homoaug_dir
+├── PDBID
+│   ├── PDBID.fasta
+│   ├── PDBID.pdb
+│   ├── PDBID_pocket.pdb
+│   ├── PDBID_pocket_chain.pdb
+│   ├── PDBID_ligand.pdb
+│   ├── PDBID_pocket_position.txt
+│   ├── rotation_matrix
+│   │   ├── (several TMalign output files)
+│   ├── *extend*
+│       ├──AugmentedPDBID1
+│       │   ├── AugmentedPDBID1_protein.pdb
+│       │   ├── AugmentedPDBID1_pocket.pdb
+│       ├── AugmentedPDBID2
+│       │   ├── AugmentedPDBID2_protein.pdb
+│       │   ├── AugmentedPDBID2_pocket.pdb
+│       ......
+│     
+......
+```
+
+Each AugmentedPDBID in extend directory refers to an extended pocket-ligand pair.
+
+#### --fasta_file
+
+The fasta file crossponding to the id_file. Each title should have the pdbid at the beginning.
+
+e.g.:
+
+```
+>1q20_A_O00204_A_PLO 
+SDISEISQKLPGEYFRYK......
+>1q21_A_P01112_A_GDP 
+MTEYKLVVVGAGGVGKSA......
+>1q22_A_O00204_A_A3P 
+SDISEISQKLPGEYFRVP......
+......
+```
+
+#### --protein_pdb_dir
+
+CIF files of the input proteins, in cif format.
+
+Name format: <pdb_id>.cif
+
+#### --pocket_pdbs_dir
+
+PDB files of the input pockets, in pdb format.
+
+Name format: <pdb_id>.pdb
+
+#### --jackhmmer_output_dir
+
+The temporary directory for storing the output of jackhmmer.
+
+#### --n_thread
+
+Number of threads for running.
+
+#### --database_fasta_path
+
+The fasta file of the database used for augmentation database, e.g. AF2DB.
+
+#### --database_pdb_dir
+
+The pdb directory of the database used for augmentation database, e.g. AF2DB's pdb directory.
+
+#### --max_extend_num
+
+The max number of extended pocket-ligand pairs for one real pocket-ligand pair.
+
+#### --TMscore_threshold and Match_rate_threshold
+
+Only the extended pocket-ligand pairs with TMscore >= TMscore_threshold and Match_rate >= Match_rate_threshold will be kept.
+
+Match_rate is the ratio of the number of matched residues to the number of residues in the real pocket.
+
+## Note
+
+For various error scenarios, we choose to skip the HomoAug for this pocket-ligand pair, including but not limited to cases where the pocket is composed of multiple chains. Please note that even if the program executes successfully, some files may be missing in certain output directories, indicating that this pocket-ligand pair is currently not suitable for HomoAug. In summary, all valid HomoAug results will be saved in the 'extend' folder.

data/HomoAug/bin/TMalign

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data/HomoAug/bin/esl-reformat

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+size 475088

data/HomoAug/bin/jackhmmer

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+size 1165768

data/HomoAug/run_HomoAug.py

@@ -0,0 +1,838 @@
+# Copyright © 2023 Institute for AI Industry Research (AIR), Tsinghua University.
+# License: GNU GPLv3. [See details in LICENSE]
+
+import argparse
+import os
+import glob
+import Bio.PDB
+import ray
+import random
+import json
+import mmap
+import time
+import sys
+import traceback
+from ray.util.queue import Queue
+import numpy as np
+
+sys.path.append(".")
+from utils.misc import execute
+from utils.ray_tools import ProgressBar
+from tqdm import tqdm
+import pathlib
+import subprocess
+import shutil
+
+
+class JackhmmerRunner:
+    def __init__(self, database_dir, task_ids, seq_dir, output_dir, n_thread):
+        self.N_CPU_PER_THREAD = 1
+        self.n_thread = n_thread
+        self.database_dir = database_dir
+        self.task_ids = task_ids
+        self.seq_dir = seq_dir
+        self.output_dir = output_dir
+        if output_dir.endswith("/"):
+            output_dir = output_dir[:-1]
+        if not os.path.exists(output_dir):
+            os.mkdir(output_dir)
+        self.sto_dir = output_dir + "_sto"
+        if not os.path.exists(self.sto_dir):
+            os.mkdir(self.sto_dir)
+        self.a2m_dir = output_dir + "_a2m"
+        if not os.path.exists(self.a2m_dir):
+            os.mkdir(self.a2m_dir)
+
+    def split_list(_list, n):
+        chunk_size = (len(_list) - 1) // n + 1
+        chunks = [_list[i * chunk_size : (i + 1) * chunk_size] for i in range(n)]
+        return chunks
+
+    @ray.remote(num_cpus=1)
+    def process_jobs(self, id, jobs_queue, actor):
+        print("start process", id)
+        while not jobs_queue.empty():
+            job = jobs_queue.get()
+            try:
+                self.execute_one_job(job)
+
+            except:
+                print(f"failed: {job}")
+                traceback.print_exception(*sys.exc_info())
+            try:
+                actor.update.remote(1)
+            except:
+                pass
+        return 1
+
+    def execute_one_job(self, job):
+        seq_file_Path = pathlib.Path(self.seq_dir) / job[:4] / (job + ".fasta")
+        output_file_Path = pathlib.Path(self.sto_dir) / (job + ".sto")
+        execute(
+            f"jackhmmer"
+            f" --cpu {self.N_CPU_PER_THREAD}"
+            f" -A {output_file_Path}"
+            f" -o /dev/null"
+            f" -E 0.001"
+            f" -N 3"
+            f" {str(seq_file_Path)}"
+            f" {self.database_dir}"
+        )
+        # with open(output_file_Path,"w") as f:
+        #     f.write("")
+        # # sleep 1 s
+        # time.sleep(1)
+        return 1
+
+    def change_sto_to_fasta(self):
+        sto_files = glob.glob(self.sto_dir + "/*.sto")
+        for sto_path in tqdm(sto_files):
+            pdb_id = sto_path.split("/")[-1].split(".")[0]
+            # print(pdb_id)
+            a2m_path = self.a2m_dir + f"/{pdb_id}.a2m"
+            execute(
+                f"esl-reformat --informat stockholm"
+                f" -o {str(a2m_path)} a2m"
+                f" {str(sto_path)}"
+            )
+            fasta_path = self.output_dir + f"/{pdb_id}.fasta"
+            output = ""
+            with open(a2m_path) as f:
+                for line in f:
+                    line = line.strip()
+                    if line[0] != ">":
+                        output += line
+                    else:
+                        output += "\n" + line + "\n"
+            output = output.strip()
+            output = output.split("\n")[:-2]
+            output = "\n".join(output)
+            with open(fasta_path, "w") as f:
+                f.write(output)
+
+    def run_jackhmmer(self):
+        all_jobs = []
+        with open(self.task_ids, "r") as f:
+            data = f.readlines()
+            for line in data:
+                job = line.strip()
+                job = job[:4].upper()
+                all_jobs.append(job)
+        print("all jobs:", len(all_jobs))
+        uncompleted_jobs = all_jobs
+        # completed?
+        # uncompleted_jobs=[]
+        # for job in all_jobs:
+        #     if (not is_complete(job)):
+        #         uncompleted_jobs.append(job)
+        # print("uncompleted jobs:",len(uncompleted_jobs))
+        ray.init()
+        job_queue = Queue()
+        for job in tqdm(uncompleted_jobs):
+            job_queue.put(job)
+        print("job queue size:", job_queue.qsize())
+        pb = ProgressBar(len(all_jobs))
+        actor = pb.actor
+        print("actor:", actor)
+        job_id_list = []
+        self.n_thread = min(self.n_thread, len(uncompleted_jobs))
+        for i in range(self.n_thread):
+            job_id_list.append(self.process_jobs.remote(self, i, job_queue, actor))
+        pb.print_until_done()
+        result = ray.get(job_id_list)
+        print("Run homo search done!")
+        ray.shutdown()
+
+        # remove tmp folders
+        shutil.rmtree(self.a2m_dir)
+        shutil.rmtree(self.sto_dir)
+
+        # change sto to fasta
+        self.change_sto_to_fasta()
+
+
+class LigandPocketExtractor:
+    def __init__(self, id_file, homoaug_dir, n_thread):
+        self.id_file = id_file
+        self.homoaug_dir = homoaug_dir
+        self.n_thread = n_thread
+        self.read_ligand_name_chain_name()
+
+    def read_ligand_name_chain_name(self):
+        # read ligand_name
+        ligand_name = {}
+        chain_name = {}
+        with open(self.id_file, "r") as f:
+            for line in f.readlines():
+                line = line.strip()
+                id = line[:4].upper()
+                ligand_name[id] = line.split("_")[-1]
+                chain_name[id] = line.split("_")[1]
+        # print("ligand_name:",ligand_name)
+        self.ligand_name = ligand_name
+        self.chain_name = chain_name
+
+    def execute_one_job(self, job):
+        id = job
+        # print ('############################')
+        # print (id)
+        # read original pdb
+        pdb_file = self.homoaug_dir + "/" + id + "/" + id + "_protein.pdb"
+        if not os.path.exists(pdb_file):
+            print("no pdb file for id:", id)
+            return 1
+        pdb_parser = Bio.PDB.PDBParser(QUIET=True)
+        structure = pdb_parser.get_structure(id, pdb_file)
+        model = structure[0]
+        chain = model[self.chain_name[id]]
+        # get pocket_chain to a new pdb
+        pocket_chain = Bio.PDB.Chain.Chain(self.chain_name[id])
+        for residue in chain:
+            if residue.id[0] == " ":
+                pocket_chain.add(residue)
+        io = Bio.PDB.PDBIO()
+        io.set_structure(pocket_chain)
+        io.save(self.homoaug_dir + "/" + id + "/" + id + "_pocket_chain.pdb")
+
+        # save ligand to a new pdb
+        ligand_chain = Bio.PDB.Chain.Chain(self.chain_name[id])
+        ligand_found = False
+        for residue in chain:
+            if residue.resname == self.ligand_name[id]:
+                ligand_chain.add(residue)
+                ligand_found = True
+        if ligand_found:
+            io = Bio.PDB.PDBIO()
+            io.set_structure(ligand_chain)
+            io.save(self.homoaug_dir + "/" + id + "/" + id + "_ligand.pdb")
+        else:
+            print("ligand not found:", id)
+            return 1
+
+        # remove ligand in the id.pocket.pdb
+        pdb_file = self.homoaug_dir + "/" + id + "/" + id + "_pocket.pdb"
+        pdb_parser = Bio.PDB.PDBParser(QUIET=True)
+        structure = pdb_parser.get_structure(id, pdb_file)
+        model = structure[0]
+        chain = model["R"]
+        # get pocket to a new pdb
+        pocket = Bio.PDB.Chain.Chain("A")
+        for residue in chain:
+            if residue.id[0] == " ":
+                pocket.add(residue)
+        # write pocket pdb
+        io = Bio.PDB.PDBIO()
+        io.set_structure(pocket)
+        io.save(self.homoaug_dir + "/" + id + "/" + id + "_pocket.pdb")
+
+    @ray.remote(num_cpus=1)
+    def process_jobs(self, id, jobs_queue, actor):
+        print("start process", id)
+        while not jobs_queue.empty():
+            job = jobs_queue.get()
+            try:
+                self.execute_one_job(job)
+            except:
+                print(f"failed: {job}")
+                traceback.print_exception(*sys.exc_info())
+            try:
+                actor.update.remote(1)
+            except:
+                pass
+        return 1
+
+    def run(self):
+        all_jobs = glob.glob(self.homoaug_dir + "/*")
+        all_jobs = [x.split("/")[-1] for x in all_jobs]
+        uncompleted_jobs = all_jobs
+        job_queue = Queue()
+        for job in tqdm(uncompleted_jobs):
+            job_queue.put(job)
+        print("job queue size:", job_queue.qsize())
+        pb = ProgressBar(len(all_jobs))
+        actor = pb.actor
+        job_id_list = []
+        self.n_thread = min(self.n_thread, len(uncompleted_jobs))
+        for i in range(self.n_thread):
+            job_id_list.append(self.process_jobs.remote(self, i, job_queue, actor))
+        pb.print_until_done()
+        result = ray.get(job_id_list)
+        ray.shutdown()
+        print("Done!")
+
+
+class PocketPositionExtractor:
+    def __init__(self, homoaug_dir, n_thread):
+        self.homoaug_dir = homoaug_dir
+        self.n_thread = n_thread
+        self.aa_3_to_1 = {
+            "CYS": "C",
+            "ASP": "D",
+            "SER": "S",
+            "GLN": "Q",
+            "LYS": "K",
+            "ILE": "I",
+            "PRO": "P",
+            "THR": "T",
+            "PHE": "F",
+            "ASN": "N",
+            "GLY": "G",
+            "HIS": "H",
+            "LEU": "L",
+            "ARG": "R",
+            "TRP": "W",
+            "ALA": "A",
+            "VAL": "V",
+            "GLU": "E",
+            "TYR": "Y",
+            "MET": "M",
+            "MSE": "M",
+            "CME": "C",
+            "CSO": "C",
+            "UNK": "X",
+        }
+
+    def execute_one_job(self, job):
+        id = job
+        # print ('############################')
+        # print (id)
+
+        # read pocket_pdb
+        pocket_pdb_file = self.homoaug_dir + "/" + id + "/" + id + "_pocket.pdb"
+        pocket_pdb_structure = Bio.PDB.PDBParser().get_structure(id, pocket_pdb_file)
+        model = pocket_pdb_structure[0]
+        for chain in model:
+            pocket_chain_id = chain.id
+            break
+        chain = model[pocket_chain_id]
+
+        # get the pocket atom coordinates
+        pocket_atom_coordinates = set()
+        for residue in chain:
+            if residue.id[0] != " ":
+                continue
+            for atom in residue:
+                pocket_atom_coordinates.add(tuple(atom.get_coord()))
+        # print("pocket_atom_coordinates:",pocket_atom_coordinates)
+
+        sequence = ""
+        pocket_chain_pdb_file = (
+            self.homoaug_dir + "/" + id + "/" + id + "_pocket_chain.pdb"
+        )
+        # get the position of the pocket residues in the pocket chain
+        pocket_chain_structure = Bio.PDB.PDBParser().get_structure(
+            id, pocket_chain_pdb_file
+        )
+        model = pocket_chain_structure[0]
+        for chain in model:
+            pocket_chain_id = chain.id
+            break
+        chain_num = len(list(model.get_chains()))
+        if chain_num > 1:
+            print("error: more than 1 chain in pocket_pdb_file:", id)
+            return 1
+        chain = model[pocket_chain_id]
+        for residue in chain:
+            if residue.id[0] != " ":
+                continue
+            in_pocket = False
+            for atom in residue:
+                if tuple(atom.get_coord()) in pocket_atom_coordinates:
+                    in_pocket = True
+                    break
+            if in_pocket:
+                sequence += self.aa_3_to_1[residue.resname]
+            else:
+                sequence += "-"
+
+        # save
+        sequence_position_file = (
+            self.homoaug_dir + "/" + id + "/" + id + "_pocket_position.txt"
+        )
+        with open(sequence_position_file, "w") as f:
+            f.write(sequence)
+
+    @ray.remote(num_cpus=1)
+    def process_jobs(self, id, jobs_queue, actor):
+        print("start process", id)
+        while not jobs_queue.empty():
+            job = jobs_queue.get()
+            try:
+                self.execute_one_job(job)
+            except:
+                print(f"failed: {job}")
+                traceback.print_exception(*sys.exc_info())
+            try:
+                actor.update.remote(1)
+            except:
+                pass
+        return 1
+
+    def run(self):
+        all_jobs = []
+        for id in os.listdir(self.homoaug_dir):
+            if os.path.isfile(self.homoaug_dir + "/" + id + "/" + id + "_ligand.pdb"):
+                if os.path.isfile(
+                    self.homoaug_dir + "/" + id + "/" + id + "_pocket.pdb"
+                ):
+                    all_jobs.append(id)
+        uncompleted_jobs = all_jobs
+        job_queue = Queue()
+        for job in tqdm(uncompleted_jobs):
+            job_queue.put(job)
+        print("job queue size:", job_queue.qsize())
+        pb = ProgressBar(len(all_jobs))
+        actor = pb.actor
+        job_id_list = []
+        self.n_thread = min(self.n_thread, len(uncompleted_jobs))
+        for i in range(self.n_thread):
+            job_id_list.append(self.process_jobs.remote(self, i, job_queue, actor))
+        pb.print_until_done()
+        result = ray.get(job_id_list)
+        ray.shutdown()
+        print("Done!")
+
+
+class TMalignRunner:
+    def __init__(
+        self,
+        max_extend_num,
+        homoaug_dir,
+        MSA_dir,
+        AF2DB_dir,
+        TMscore_threshold,
+        Match_rate_threshold,
+        n_thread,
+    ):
+        self.n_thread = n_thread
+        self.max_extend_num = max_extend_num
+        self.homoaug_dir = homoaug_dir
+        self.MSA_dir = MSA_dir
+        self.AF2DB_dir = AF2DB_dir
+        self.TMscore_threshold = TMscore_threshold
+        self.Match_rate_threshold = Match_rate_threshold
+
+    def _remove_gap_of_primary_sequence(self, primary_sequence, candidate_sequence):
+        assert len(primary_sequence) == len(candidate_sequence)
+        primary_sequence_without_gap = ""
+        candidate_sequence_without_gap = ""
+        for i in range(len(primary_sequence)):
+            if primary_sequence[i] != "-":
+                primary_sequence_without_gap += primary_sequence[i]
+                candidate_sequence_without_gap += candidate_sequence[i]
+        return primary_sequence_without_gap, candidate_sequence_without_gap
+
+    def _calc_match_rate(self, pocket_position, Aligned_seq):
+        total_cnt = 0
+        match_cnt = 0
+        for i in range(len(Aligned_seq)):
+            if pocket_position[i] != "-":
+                total_cnt += 1
+                if pocket_position[i] == Aligned_seq[i]:
+                    match_cnt += 1
+        return match_cnt / total_cnt
+
+    def _get_rotate_matrix(self, rotate_matrix_file):
+        with open(rotate_matrix_file, "r") as f:
+            data = f.readlines()
+        u = []
+        t = []
+        for i in range(2, 5):
+            line = data[i].split(" ")
+            line_float = [float(x) for x in line if x != ""]
+            t.append(line_float[1])
+            u.append(line_float[2:])
+        u = np.array(u)
+        t = np.array(t)
+        return u, t
+
+    def _read_ligand_pdb(self, ligand_pdb_file):
+        parser = Bio.PDB.PDBParser()
+        structure = parser.get_structure("ligand", ligand_pdb_file)
+        ligand_coords = []
+        for model in structure:
+            for chain in model:
+                for residue in chain:
+                    for atom in residue:
+                        ligand_coords.append(atom.get_coord())
+        return ligand_coords
+
+    @ray.remote(num_cpus=1)
+    def process_jobs(self, id, jobs_queue, actor):
+        print("start process", id)
+        while not jobs_queue.empty():
+            job = jobs_queue.get()
+            try:
+                self.execute_one_job(job)
+
+            except:
+                print(f"failed: {job}")
+                traceback.print_exception(*sys.exc_info())
+            try:
+                actor.update.remote(1)
+            except:
+                pass
+        return 1
+
+    def execute_one_job(self, id):
+        # print("#######################")
+        print(id)
+        # get the sequence from pdb
+        fasta_dir = self.homoaug_dir + "/" + id + "/" + id + ".fasta"
+        with open(fasta_dir) as f:
+            fasta = f.readlines()
+
+        # read the pocket position
+        pocket_position_file = (
+            self.homoaug_dir + "/" + id + "/" + id + "_pocket_position.txt"
+        )
+        if not os.path.exists(pocket_position_file):
+            print("position_file not exist")
+            return 1
+        with open(pocket_position_file) as f:
+            pocket_position = f.readline().strip()
+
+        # create rotation matrix dir
+        rotation_matrix_dir = self.homoaug_dir + "/" + id + "/" + "rotation_matrix/"
+        if not os.path.exists(rotation_matrix_dir):
+            os.makedirs(rotation_matrix_dir)
+
+        # get the sequence from TMalign
+        chain_pdb_file = self.homoaug_dir + "/" + id + "/" + id + "_pocket_chain.pdb"
+        MSA_file = self.MSA_dir + f"/{id}" + ".fasta"
+        if not os.path.exists(MSA_file):
+            print("MSA_file not exist")
+            return 1
+        MSA_ids = []
+        with open(MSA_file) as f:
+            lines = f.readlines()
+            for idx in range(0, len(lines), 2):
+                MSA_ids.append(lines[idx].strip().split(" ")[-1])
+
+        # create extend dir
+        extend_dir = self.homoaug_dir + "/" + id + "/" + "extend"
+        if not os.path.exists(extend_dir):
+            os.mkdir(extend_dir)
+
+        # get ligand
+        ligand_file = self.homoaug_dir + "/" + id + "/" + id + "_ligand.pdb"
+        ligand_coords = self._read_ligand_pdb(ligand_file)
+
+        # TMalign
+        cnt = len(glob.glob(extend_dir + "/*"))
+        for MSA_id in list(MSA_ids):
+            if cnt >= self.max_extend_num:
+                break
+            # calculate TMscore
+            MSA_pdb_file = self.AF2DB_dir + f"/{MSA_id}.pdb"
+            if not os.path.exists(MSA_pdb_file):
+                continue
+            rotation_matrix_file = rotation_matrix_dir + f"{MSA_id}.txt"
+            if os.path.exists(rotation_matrix_file):
+                continue
+            out_bytes = subprocess.check_output(
+                ["TMalign", MSA_pdb_file, chain_pdb_file, "-m", rotation_matrix_file]
+            )
+            out_text = out_bytes.decode("utf-8").strip().split("\n")
+            TMscore1 = float(out_text[12].split(" ")[1])
+            TMscore2 = float(out_text[13].split(" ")[1])
+            (
+                sequence_from_TMalign,
+                MSA_aligned_sequence,
+            ) = self._remove_gap_of_primary_sequence(out_text[19], out_text[17])
+            TMalign_file = rotation_matrix_dir + f"{MSA_id}_TMscore.txt"
+            with open(TMalign_file, "w") as f:
+                f.write("TMscore normalized to chain_pdb:" + str(TMscore2) + "\n")
+                f.write("TMscore normalized to MSA_pdb:" + str(TMscore1) + "\n")
+                f.write("Aligned sequence : \n")
+                f.write(sequence_from_TMalign + "\n")
+                f.write(MSA_aligned_sequence + "\n")
+            TMscore = TMscore2
+
+            # calculate Match_score
+            Aligned_seq = MSA_aligned_sequence
+            Match_rate = self._calc_match_rate(pocket_position, Aligned_seq)
+
+            # print("MSA_id:",MSA_id)
+            # print("TMscore:",TMscore)
+            # print("Match_rate:",Match_rate)
+            if (
+                TMscore >= self.TMscore_threshold
+                and Match_rate >= self.Match_rate_threshold
+            ):
+                extend_instance_dir = extend_dir + "/" + MSA_id + "/"
+                # if os.path.exists(extend_instance_dir):
+                #     continue
+                os.mkdir(extend_instance_dir)
+
+                # read ori MSA pdb file
+                MSA_pdb_file = self.AF2DB_dir + f"/{MSA_id}" + ".pdb"
+                parser = Bio.PDB.PDBParser()
+                structure = parser.get_structure(MSA_id, MSA_pdb_file)
+                model = structure[0]
+                for chain in model:
+                    MSA_chain_id = chain.id
+                    break
+                MSA_chain = model[MSA_chain_id]
+
+                # get rotate_matrix
+                rotation_matrix_file = rotation_matrix_dir + f"{MSA_id}.txt"
+                rotation_matrix = self._get_rotate_matrix(rotation_matrix_file)
+
+                for residue in MSA_chain:
+                    for atom in residue:
+                        coord = atom.get_coord()
+                        coord = np.array(coord)
+                        new_coord = (
+                            np.dot(rotation_matrix[0], coord) + rotation_matrix[1]
+                        )
+                        atom.set_coord(new_coord)
+
+                # write new pdb file
+                io = Bio.PDB.PDBIO()
+                io.set_structure(structure)
+                io.save(extend_instance_dir + f"{MSA_id}" + "_protein.pdb")
+
+                # get pocket , which is in the 6A of ligand
+                MSA_pocket_file = extend_instance_dir + f"{MSA_id}" + "_pocket.pdb"
+                for residue in MSA_chain:
+                    remove_atom_ids = []
+                    for atom in residue:
+                        # print("atom: ",atom.id)
+                        coord = atom.get_coord()
+                        f = 0
+                        for ligand_coord in ligand_coords:
+                            dis = np.linalg.norm(coord - ligand_coord)
+                            if np.linalg.norm(coord - ligand_coord) <= 6:
+                                f = 1
+                                break
+                        if f == 0:
+                            remove_atom_ids.append(atom.id)
+                    for atom_id in remove_atom_ids:
+                        residue.detach_child(atom_id)
+                io = Bio.PDB.PDBIO()
+                io.set_structure(structure)
+                io.save(MSA_pocket_file)
+                cnt += 1
+        print("finish: pdb_id:", id)
+        return 1
+
+    def run(self):
+        all_jobs = []
+        for id in os.listdir(self.homoaug_dir):
+            if (
+                os.path.isfile(self.homoaug_dir + "/" + id + "/" + id + "_ligand.pdb")
+                and os.path.isfile(
+                    self.homoaug_dir + "/" + id + "/" + id + "_pocket.pdb"
+                )
+                and os.path.isfile(
+                    self.homoaug_dir + "/" + id + "/" + id + "_pocket_position.txt"
+                )
+            ):
+                all_jobs.append(id)
+        uncompleted_jobs = all_jobs
+        job_queue = Queue()
+        for job in tqdm(uncompleted_jobs):
+            job_queue.put(job)
+        print("job queue size:", job_queue.qsize())
+        pb = ProgressBar(len(all_jobs))
+        actor = pb.actor
+        job_id_list = []
+        self.n_thread = min(self.n_thread, len(uncompleted_jobs))
+        for i in range(self.n_thread):
+            job_id_list.append(self.process_jobs.remote(self, i, job_queue, actor))
+        pb.print_until_done()
+        result = ray.get(job_id_list)
+        ray.shutdown()
+        print("Done!")
+
+
+class HomoAugRunner:
+    def __init__(self, args):
+        self.id_file = args.id_file
+        self.homoaug_dir = args.homoaug_dir
+        self.fasta_file = args.fasta_file
+        self.protein_pdb_dir = args.protein_pdb_dir
+        self.pocket_pdbs_dir = args.pocket_pdbs_dir
+        self.jackhmmer_output_dir = args.jackhmmer_output_dir
+        self.database_fasta_path = args.database_fasta_path
+        self.max_extend_num = args.max_extend_num
+        self.database_pdb_dir = args.database_pdb_dir
+        self.TMscore_threshold = args.TMscore_threshold
+        self.Match_rate_threshold = args.Match_rate_threshold
+        self.n_thread = args.n_thread
+
+    def read_dataset_fasta(self):
+        protein_seq = {}
+        with open(self.fasta_file) as f:
+            fasta = f.readlines()
+            for i in range(0, len(fasta), 2):
+                id = fasta[i].strip()
+                id = id[1:5].upper()
+                seq = fasta[i + 1].strip()
+                protein_seq[id] = seq
+        return protein_seq
+
+    def create_dir(self):
+        # Create homoaug dir and subdirs
+        # Dir format
+        # homoaug_dir
+        # └── id
+        #     ├── id.fasta
+        #     └── id_protein.pdb
+        #     └── id_pocket.pdb
+
+        protein_seq = self.read_dataset_fasta()
+        if not os.path.exists(self.homoaug_dir):
+            os.mkdir(self.homoaug_dir)
+        with open(self.id_file, "r") as f:
+            lines = f.readlines()
+        for line in tqdm(lines):
+            id = line.strip()
+            id = id[:4].upper()
+            if not os.path.exists(self.homoaug_dir + "/" + id):
+                os.mkdir(self.homoaug_dir + "/" + id)
+
+                # create fasta
+                with open(self.homoaug_dir + "/" + id + "/" + id + ".fasta", "w") as f:
+                    f.write(">" + id + "\n" + protein_seq[id] + "\n")
+
+                # copy pdb
+                cif_file = glob.glob(self.protein_pdb_dir + "/" + id + "*")
+                if len(cif_file) != 1:
+                    print("error : having more than 1 cif file", id)
+                    continue
+                cif_file = cif_file[0]
+                # read cif
+                parser = Bio.PDB.MMCIFParser(QUIET=True)
+                structure = parser.get_structure(id, cif_file)
+                # get number of models
+                n_model = len(structure)
+                if n_model != 1:
+                    print("error : having more than 1 model", id)
+                    continue
+                # get number of chains
+                n_chain = len(list(structure.get_chains()))
+                if n_chain != 1:
+                    print("error : having more than 1 chain", id)
+                    continue
+                # save pdb
+                io = Bio.PDB.PDBIO()
+                io.set_structure(structure)
+                io.save(self.homoaug_dir + "/" + id + "/" + id + "_protein.pdb")
+                # os.system("cp "+homoaug_dir+"/"+id+"/"+id+"_protein.pdb "+homoaug_dir+"/"+id+"/"+id+"_pocket_chain.pdb")
+
+                # copy pocket
+                pocket_file = glob.glob(self.pocket_pdbs_dir + "/" + id + "*")
+                if len(pocket_file) != 1:
+                    print("error : having more than 1 pocket file", id)
+                    continue
+                pocket_file = pocket_file[0]
+                # copy to homoaug
+                os.system(
+                    "cp "
+                    + pocket_file
+                    + " "
+                    + self.homoaug_dir
+                    + "/"
+                    + id
+                    + "/"
+                    + id
+                    + "_pocket.pdb"
+                )
+
+    def run(self):
+        self.create_dir()
+
+        # Run jackhmmer
+        print("# Start running jackhmmer")
+        jackhmmer_runner = JackhmmerRunner(
+            database_dir=self.database_fasta_path,
+            task_ids=self.id_file,
+            seq_dir=self.homoaug_dir,
+            output_dir=self.jackhmmer_output_dir,
+            n_thread=self.n_thread,
+        )
+        jackhmmer_runner.run_jackhmmer()
+
+        print("# Start running ligand pocket extractor")
+        ligand_pocket_extractor = LigandPocketExtractor(
+            self.id_file, self.homoaug_dir, self.n_thread
+        )
+        ligand_pocket_extractor.run()
+
+        print("# Start running pocket position extractor")
+        pocket_position_extractor = PocketPositionExtractor(
+            self.homoaug_dir, self.n_thread
+        )
+        pocket_position_extractor.run()
+
+        print("# Start running TMalign")
+        tmalign_runner = TMalignRunner(
+            self.max_extend_num,
+            self.homoaug_dir,
+            self.jackhmmer_output_dir,
+            self.database_pdb_dir,
+            self.TMscore_threshold,
+            self.Match_rate_threshold,
+            self.n_thread,
+        )
+        tmalign_runner.run()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--id_file", type=str, default="/drug/BioLip/tmp.id")
+    parser.add_argument("--homoaug_dir", type=str, default="/drug/BioLip/homoaug_new")
+    parser.add_argument(
+        "--fasta_file",
+        type=str,
+        default="/drug/BioLip/BioLiP_v2023-04-13_regularLigand.fasta",
+    )
+    parser.add_argument(
+        "--protein_pdb_dir", type=str, default="/drug/BioLip/protein_pdb"
+    )
+    parser.add_argument(
+        "--pocket_pdbs_dir", type=str, default="/drug/BioLip/pocket_pdb"
+    )
+    parser.add_argument(
+        "--jackhmmer_output_dir", type=str, default="/drug/BioLip/pdbbind_MSA_fasta"
+    )
+    parser.add_argument(
+        "--n_thread", type=int, default=10, help="number of threads for running"
+    )
+    parser.add_argument(
+        "--database_fasta_path",
+        type=str,
+        default="/data/protein/AF2DB/AFDB_HC_50.fa",
+        help="jackhmmer search database, in fasta format",
+    )
+    parser.add_argument(
+        "--database_pdb_dir",
+        type=str,
+        default="/drug/AFDB_HC_50_PDB",
+        help="homoaug search database, e.g. AF2DB",
+    )
+    parser.add_argument(
+        "--max_extend_num",
+        type=int,
+        default=20,
+        help="max number of extended pocket-ligand pairs for one real pocket-ligand pair",
+    )
+    parser.add_argument(
+        "--TMscore_threshold",
+        type=float,
+        default=0.4,
+        help="TMscore threshold for extending",
+    )
+    parser.add_argument(
+        "--Match_rate_threshold",
+        type=float,
+        default=0.4,
+        help="Match_rate threshold for extending",
+    )
+
+    args = parser.parse_args()
+    homoaug_runner = HomoAugRunner(args)
+    homoaug_runner.run()
+    print("HomoAug Done!")

data/HomoAug/utils/misc.py

@@ -0,0 +1,111 @@
+import hashlib
+import os
+import signal
+import subprocess
+import sys
+import time
+
+
+def time2str(time_used):
+    gaps = [
+        ('days', 86400000),
+        ('h', 3600000),
+        ('min', 60000),
+        ('s', 1000),
+        ('ms', 1)
+    ]
+    time_used *= 1000
+    time_str = []
+    for unit, gap in gaps:
+        val = time_used // gap
+        if val > 0:
+            time_str.append('{}{}'.format(int(val), unit))
+            time_used -= val * gap
+    if len(time_str) == 0:
+        time_str.append('0ms')
+    return ' '.join(time_str)
+
+
+def get_date():
+    return time.strftime('%Y-%m-%d', time.localtime(time.time()))
+
+def get_time(t=None):
+    if t is None:
+        t = time.time()
+    return time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(t))
+
+
+def hash_seed(*items, width=32):
+    # width: range of seed: [0, 2**width)
+    sha = hashlib.sha256()
+    for item in items:
+        sha.update(str(item).encode('utf-8'))
+    return int(sha.hexdigest()[23:23+width//4], 16)
+
+
+def execute(command, verbose=False, cmd_out_off=False):
+    if verbose:
+        print(command)
+        print()
+    if cmd_out_off:
+        popen = silent(subprocess.Popen)
+    else:
+        popen = subprocess.Popen
+    p = popen(command, shell=True)
+    try:
+        p.wait()
+    except KeyboardInterrupt:
+        try:
+            os.kill(p.pid, signal.SIGINT)
+        except OSError:
+            pass
+        p.wait()
+
+
+def silent(func):
+    """
+    Make function silent.
+    Useful for closing the output if you don't find a switch.
+    """
+    def wrap_silent(*args, **kwargs):
+        fd = os.dup(1)
+        sys.stdout.flush()
+        os.close(1)
+        os.open(os.devnull, os.O_WRONLY)
+
+        res = func(*args, **kwargs)
+
+        os.close(1)
+        os.dup(fd)
+        os.close(fd)
+
+        return res
+    return wrap_silent
+
+
+def with_time(func, pretty_time=False):
+    """
+    Usage:
+
+    1. as a function decorator
+    ``` python
+    @with_time
+    def func(...):
+        ...
+    result, cost_in_seconds = func(...)
+    ```
+
+    2. directly apply
+    ``` python
+    result, cost_in_seconds = with_time(func)(...)
+    ```
+    """
+    def wrap_time(*args, **kwargs):
+        start = time.time()
+        res = func(*args, **kwargs)
+        time_cost = time.time() - start
+        if pretty_time:
+            time_cost = time2str(time_cost)
+        return res, time_cost
+    return wrap_time
+

data/HomoAug/utils/ray_tools.py

@@ -0,0 +1,90 @@
+from asyncio import Event
+from typing import Tuple
+
+import ray
+from ray.actor import ActorHandle
+from tqdm import tqdm
+
+
+def split_list(_list, n):
+    chunk_size = (len(_list) - 1) // n + 1
+    chunks = [_list[i * chunk_size : (i + 1) * chunk_size] for i in range(n)]
+    return chunks
+
+
+@ray.remote
+class ProgressBarActor:
+    counter: int
+    delta: int
+    event: Event
+
+    def __init__(self) -> None:
+        self.counter = 0
+        self.delta = 0
+        self.event = Event()
+
+    def update(self, num_items_completed: int) -> None:
+        """Updates the ProgressBar with the incremental
+        number of items that were just completed.
+        """
+        self.counter += num_items_completed
+        self.delta += num_items_completed
+        self.event.set()
+
+    async def wait_for_update(self) -> Tuple[int, int]:
+        """Blocking call.
+
+        Waits until somebody calls `update`, then returns a tuple of
+        the number of updates since the last call to
+        `wait_for_update`, and the total number of completed items.
+        """
+        await self.event.wait()
+        self.event.clear()
+        saved_delta = self.delta
+        self.delta = 0
+        return saved_delta, self.counter
+
+    def get_counter(self) -> int:
+        """
+        Returns the total number of complete items.
+        """
+        return self.counter
+
+
+class ProgressBar:
+    progress_actor: ActorHandle
+    total: int
+    description: str
+    pbar: tqdm
+
+    def __init__(self, total: int, description: str = ""):
+        # Ray actors don't seem to play nice with mypy, generating
+        # a spurious warning for the following line,
+        # which we need to suppress. The code is fine.
+        self.progress_actor = ProgressBarActor.remote()  # type: ignore
+        self.total = total
+        print("Total:", total)
+        self.description = description
+
+    @property
+    def actor(self) -> ActorHandle:
+        """Returns a reference to the remote `ProgressBarActor`.
+
+        When you complete tasks, call `update` on the actor.
+        """
+        return self.progress_actor
+
+    def print_until_done(self) -> None:
+        """Blocking call.
+
+        Do this after starting a series of remote Ray tasks, to which you've
+        passed the actor handle. Each of them calls `update` on the actor.
+        When the progress meter reaches 100%, this method returns.
+        """
+        pbar = tqdm(desc=self.description, total=self.total, ncols=80)
+        while True:
+            delta, counter = ray.get(self.actor.wait_for_update.remote())
+            pbar.update(delta)
+            if counter >= self.total:
+                pbar.close()
+                return

data/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 DP Technology
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

data/README.md

@@ -0,0 +1,122 @@
+<div align="center">
+  <img src="docs/logo.png" alt="BindCLIP logo" width="500">
+  <h2>
+    A Unified Contrastive–Generative Representation Learning Framework<br>
+    for Virtual Screening
+  </h2>
+</div>
+
+<p align="center">
+   Official implementation of BindCLIP (<strong>KDD 2026 Oral</strong>)
+</p>
+
+<p align="center">
+  <a href="https://dl.acm.org/doi/10.1145/3770855.3819040">📄 Paper</a> •
+  <a href="./LICENSE">📜 License</a>
+</p>
+
+
+<p align="center">
+  <img src="docs/framework.jpg" alt="BindCLIP framework" width="950">
+</p>
+
+# Requirements
+
+```bash
+conda create -n bindclip python=3.10
+conda activate bindclip
+conda install pytorch=2.0 pytorch-cuda=11.8 -c pytorch -c nvidia
+# Verify PyTorch and CUDA installation
+# python -c "import torch; print(torch.__version__); print(torch.version.cuda)"
+
+conda install pyg=2.3.1 -c pyg
+conda install "rdkit=2022.09.5" openbabel tensorboard pyyaml easydict python-lmdb -c conda-forge
+# [IMPORTANT]: RDKit 2022.09.5 is required.
+# Using a different version may require reprocessing the training data.
+
+# Install Uni-Core
+cd Uni-Core-main
+python setup.py install
+
+# Additional dependencies
+pip install tokenizers==0.13.3
+pip install ml-collections tensorboardX wandb
+pip install ipython
+pip install torch-scatter -f https://data.pyg.org/whl/torch-2.0.0+cu118.html
+pip install torch-sparse -f https://data.pyg.org/whl/torch-2.0.1+cu118.html
+pip install torch-cluster -f https://data.pyg.org/whl/torch-2.0.1+cu118.html
+conda install biopandas
+```
+
+# Data
+
+## Training Data
+
+We release the training data used in BindCLIP, together with the external molecular library used for negative augmentation:
+
+https://drive.google.com/drive/folders/1F48zwXEtmAgwGGuKCXOMFR21U8fxEAJh?usp=sharing
+
+The released training data contains two versions corresponding to the hard-negative construction strategies described in the paper:
+
+- Random Sampling
+- UniMol Retrieval
+
+**NOTE:** We use the same training/validation split as DrugCLIP. The only difference is that we additionally provide a binding pose and a negative molecule for each pair.
+
+## Evaluation Data
+
+We follow the DrugCLIP evaluation setup for virtual screening benchmarks, including: LIT-PCBA and DUD-E
+
+The processed benchmark data can be downloaded from:
+
+https://drive.google.com/drive/folders/1zW1MGpgunynFxTKXC2Q4RgWxZmg6CInV?usp=sharing
+
+# Checkpoints
+
+We release two pretrained checkpoints:
+
+https://drive.google.com/drive/folders/1KKID5DU_hh2e5sE5Xmem10lfSy_qWwIE?usp=sharing
+
+The released checkpoints correspond to two different negative sampling strategies used during training:
+
+- Random Negative Augmentation
+- Hard Negative Augmentation
+
+We provide both versions because we observed that the random-negative model can sometimes exhibit competitive or even better generalization performance depending on the downstream application. We encourage users to evaluate both checkpoints and choose the one that works best for their specific use case.
+
+**NOTE: Our checkpoints are fully compatible with the DrugCLIP inference code. Users can directly replace the DrugCLIP checkpoint with a BindCLIP checkpoint without any code modifications.**
+
+# Train
+
+```bash
+bash bindclip.sh
+```
+
+# Test
+
+```bash
+bash test.sh
+```
+
+# Retrieval
+
+```bash
+bash retrieval.sh
+```
+
+Example files (`pocket.lmdb` and `mols.lmdb`) can be found in the DrugCLIP Google Drive folder under the `retrieval/` directory.
+
+# Citation
+
+If you find this work useful, please consider citing:
+
+```bibtex
+@inproceedings{qiao2026bindclip,
+  title={BindCLIP: A Unified Contrastive-Generative Representation Learning Framework for Virtual Screening},
+  author={Qiao, Anjie and Wang, Zhen and Li, Yaliang and Rao, Jiahua and Yang, Yuedong},
+  year={2026},
+  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
+  pages = {11854–11865},
+  numpages = {12}
+}
+```

data/bindclip.sh

@@ -0,0 +1,44 @@
+
+
+data_path="data/train_no_test_af"
+
+param_dir="hingeloss_lmdba1.5_seed42"
+save_dir="ckpt/savedir${param_dir}"
+
+tmp_save_dir="ckpt/tmp/tmp_save_dir${param_dir}"
+tsb_dir="ckpt/tsb/tsb_dir${param_dir}"
+
+n_gpu=1
+MASTER_PORT=10055
+finetune_mol_model="unimol/ckp/mol_pre_no_h_220816.pt" # unimol pretrained mol model
+finetune_pocket_model="unimol/ckp/pocket_pre_220816.pt" # unimol pretrained pocket model
+
+
+batch_size=48
+batch_size_valid=64
+epoch=200
+dropout=0.0
+warmup=0.06
+update_freq=4
+dist_threshold=8.0
+recycling=3
+lr=1e-3
+
+export NCCL_ASYNC_ERROR_HANDLING=1
+export OMP_NUM_THREADS=1
+CUDA_VISIBLE_DEVICES="0"  $(which unicore-train) $data_path --user-dir ./unimol --train-subset train --valid-subset valid \
+       --num-workers 0 --ddp-backend=c10d \
+       --task drugclip --loss in_batch_softmax_hinge --arch drugclip  \
+       --max-pocket-atoms 256 \
+       --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-8 --clip-norm 1.0 \
+       --lr-scheduler polynomial_decay --lr $lr --warmup-ratio $warmup --max-epoch $epoch --batch-size $batch_size --batch-size-valid $batch_size_valid \
+       --fp16 --fp16-init-scale 4 --fp16-scale-window 256 --update-freq $update_freq --seed 42 \
+       --tensorboard-logdir $tsb_dir \
+       --log-interval 100 --log-format simple \
+       --validate-interval 1 \
+       --best-checkpoint-metric valid_bedroc --patience 2000 --all-gather-list-size 2048000 \
+       --save-dir $save_dir --tmp-save-dir $tmp_save_dir --keep-last-epochs 5 \
+       --find-unused-parameters \
+       --maximize-best-checkpoint-metric \
+       --finetune-pocket-model $finetune_pocket_model \
+       --finetune-mol-model $finetune_mol_model \

data/encode_ligand_atom.sh

@@ -0,0 +1,14 @@
+results_path="./test"  # replace to your results path
+batch_size=8
+weight_path="ckpt/savedirrandom_zinc/checkpoint_best_seed-1.pt"
+SAVE_DIR="./motivation_analysis_1/sampled_subset/" # path to the cached mol embedding file
+
+CUDA_VISIBLE_DEVICES="0" python ./unimol/encode_ligand_atom.py --user-dir ./unimol $data_path "./data" --valid-subset test \
+       --results-path $results_path \
+       --num-workers 8 --ddp-backend=c10d --batch-size $batch_size \
+       --task drugclip --loss in_batch_softmax --arch drugclip  \
+       --max-pocket-atoms 256 \
+       --fp16 --fp16-init-scale 4 --fp16-scale-window 256  --seed 1 \
+       --path $weight_path \
+       --log-interval 100 --log-format simple \
+       --save-dir $SAVE_DIR \

data/py_scripts/lmdb_utils.py

@@ -0,0 +1,74 @@
+import os
+import pickle
+import lmdb
+from tqdm import tqdm, trange
+
+
+
+
+
+
+
+def read_lmdb(lmdb_path):
+    env = lmdb.open(
+        lmdb_path,
+        subdir=False,
+        readonly=True,
+        lock=False,
+        readahead=False,
+        meminit=False,
+        max_readers=256,
+    )  
+    txn = env.begin()
+    keys = list(txn.cursor().iternext(values=False))
+    out_list = []
+    for idx in tqdm(keys):
+        datapoint_pickled = txn.get(idx)
+        data = pickle.loads(datapoint_pickled)
+        out_list.append(data)
+        #print(len(data["coordinates"]))
+    env.close()
+    return out_list 
+
+
+
+def write_lmdb(out_list, save_path):
+    
+    env = lmdb.open(
+        save_path,
+        subdir=False,
+        lock=False,
+        readahead=False,
+        meminit=False,
+        max_readers=64,
+        map_size=1099511627776
+    )
+
+    with env.begin(write=True) as lmdb_txn:
+        for i in tqdm(range(len(out_list))):
+            lmdb_txn.put(str(i).encode('ascii'), pickle.dumps(out_list[i]))
+
+
+
+if __name__ == "__main__":
+    # Example usage
+    # Read LMDB
+    lmdb_path = "/HOME/nsccgz_ywang/nsccgz_ywang_wzh/HDD_POOL/qiaoaj/DrugCLIP/data/train_no_test_af/train.lmdb"
+    data = read_lmdb(lmdb_path)
+    print(len(data))
+    print(data[0].keys())
+    #print(len(data[1]["coordinates"]))
+
+    dic = {
+        "atoms": "atom types for each atom in the ligand",
+        "coordinates": "3D coordinates for each atom in the ligand generated by RDKit. Max number of conformations is 10",
+        "pocket_atoms": "atom types for each atom in the pocket",
+        "pocket_coordinates": "3D coordinates for each atom in the pocket",
+        "mol": "RDKit molecule object for the ligand",
+        "smi": "SMILES string for the ligand",
+        "pocket": "pdbid of the pocket",
+    }
+
+    
+
+    

data/py_scripts/write_data_with_docked.py

@@ -0,0 +1,237 @@
+import argparse
+import gzip
+import multiprocessing as mp
+import os
+import pickle
+import random
+
+import lmdb
+import numpy as np
+import pandas as pd
+import rdkit
+import rdkit.Chem.AllChem as AllChem
+import torch
+import tqdm
+from biopandas.mol2 import PandasMol2
+from biopandas.pdb import PandasPdb
+from rdkit import Chem, RDLogger
+from rdkit.Chem.MolStandardize import rdMolStandardize
+
+RDLogger.DisableLog('rdApp.*')
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--mol_data_path', type=str, default='/data/protein/DUD-E/raw/all')
+parser.add_argument('--lmdb_path', type=str, default='docked_dude_fromweb2D.lmdb')
+args = parser.parse_args()
+
+
+def gen_conformation(mol, num_conf=20, num_worker=8):
+    try:
+        mol = Chem.AddHs(mol)
+        AllChem.EmbedMultipleConfs(mol, numConfs=num_conf, numThreads=num_worker, pruneRmsThresh=1, maxAttempts=10000, useRandomCoords=False)
+        try:
+            AllChem.MMFFOptimizeMoleculeConfs(mol, numThreads=num_worker)
+        except:
+            pass
+        mol = Chem.RemoveHs(mol)
+    except:
+        print("cannot gen conf", Chem.MolToSmiles(mol))
+        return None
+    if mol.GetNumConformers() == 0:
+        print("cannot gen conf", Chem.MolToSmiles(mol))
+        return None
+    return mol
+
+def convert_2Dmol_to_data(mol, num_conf=10, num_worker=5):
+    #to 3D
+    mol = gen_conformation(mol, num_conf, num_worker)
+    if mol is None:
+        return None
+    coords = [np.array(mol.GetConformer(i).GetPositions()) for i in range(mol.GetNumConformers())]
+    atom_types = [a.GetSymbol() for a in mol.GetAtoms()]
+    return {'coords': coords, 'atom_types': atom_types, 'smi': Chem.MolToSmiles(mol), 'mol': mol}
+
+def convert_3Dmol_to_data(mol):
+
+    if mol is None:
+        return None
+    coords = [np.array(mol.GetConformer(i).GetPositions()) for i in range(mol.GetNumConformers())]
+    atom_types = [a.GetSymbol() for a in mol.GetAtoms()]
+    return {'coords': coords, 'atom_types': atom_types, 'smi': Chem.MolToSmiles(mol), 'mol': mol}
+
+def read_pdb(path):
+    pdb_df = PandasPdb().read_pdb(path)
+
+    coord = pdb_df.df['ATOM'][['x_coord', 'y_coord', 'z_coord']]
+    atom_type = pdb_df.df['ATOM']['atom_name']
+    residue_name = pdb_df.df['ATOM']['chain_id'] + pdb_df.df['ATOM']['residue_number'].astype(str)
+    residue_type = pdb_df.df['ATOM']['residue_name']
+    protein = {'coord': np.array(coord), 
+               'atom_type': list(atom_type),
+               'residue_name': list(residue_name),
+               'residue_type': list(residue_type)}
+    return protein
+
+
+def read_sdf_gz_3d(path):
+    inf = gzip.open(path)
+    with Chem.ForwardSDMolSupplier(inf, removeHs=False, sanitize=False) as gzsuppl:
+        ms = [add_charges(x) for x in gzsuppl if x is not None]
+    ms = [rdMolStandardize.Uncharger().uncharge(Chem.RemoveHs(m)) for m in ms if m is not None]
+    return ms
+
+def add_charges(m):
+    m.UpdatePropertyCache(strict=False)
+    ps = Chem.DetectChemistryProblems(m)
+    if not ps:
+        Chem.SanitizeMol(m)
+        return m
+    for p in ps:
+        if p.GetType()=='AtomValenceException':
+            at = m.GetAtomWithIdx(p.GetAtomIdx())
+            if at.GetAtomicNum()==7 and at.GetFormalCharge()==0 and at.GetExplicitValence()==4:
+                at.SetFormalCharge(1)
+            if at.GetAtomicNum()==6 and at.GetExplicitValence()==5:
+                #remove a bond
+                for b in at.GetBonds():
+                    if b.GetBondType()==Chem.rdchem.BondType.DOUBLE:
+                        b.SetBondType(Chem.rdchem.BondType.SINGLE)
+                        break
+            if at.GetAtomicNum()==8 and at.GetFormalCharge()==0 and at.GetExplicitValence()==3:
+                at.SetFormalCharge(1)
+            if at.GetAtomicNum()==5 and at.GetFormalCharge()==0 and at.GetExplicitValence()==4:
+                at.SetFormalCharge(-1)
+    try:
+        Chem.SanitizeMol(m)
+    except:
+        return None
+    return m
+
+def get_different_raid(protein, ligand, raid=6):
+    protein_coord = protein['coord']
+    ligand_coord = ligand['coord']
+    protein_residue_name = protein['residue_name']
+    pocket_residue = set()
+    for i in range(len(protein_coord)):
+        for j in range(len(ligand_coord)):
+            if np.linalg.norm(protein_coord[i] - ligand_coord[j]) < raid:
+                pocket_residue.add(protein_residue_name[i])
+    return pocket_residue
+
+def read_mol2_ligand(path):
+    mol2_df = PandasMol2().read_mol2(path)
+    coord = mol2_df.df[['x', 'y', 'z']]
+    atom_type = mol2_df.df['atom_name']
+    ligand = {'coord': np.array(coord), 'atom_type': list(atom_type), 'mol': Chem.MolFromMol2File(path)}
+    return ligand
+
+def read_smi_mol(path):
+    with open(path, 'r') as f:
+        mols_lines = list(f.readlines())
+    smis = [l.split(' ')[0] for l in mols_lines]
+    mols = [Chem.MolFromSmiles(m) for m in smis]
+    return mols
+
+def parser(protein_path, mol_path, ligand_path, activity, pocket_index, raid=6):
+    protein = read_pdb(protein_path)
+    data_mols = read_smi_mol(mol_path)
+
+    ligand = read_mol2_ligand(ligand_path)
+    pocket_residue = get_different_raid(protein, ligand, raid=raid)
+    pocket_atom_idx = [i for i, r in enumerate(protein['residue_name']) if r in pocket_residue]
+    pocket_atom_type = [protein['atom_type'][i] for i in pocket_atom_idx]
+    pocket_coord = [protein['coord'][i] for i in pocket_atom_idx]
+    pocket_residue_type = [protein['residue_type'][i] for i in pocket_atom_idx]
+    pocket_name = protein_path.split('/')[-2]
+    pool = mp.Pool(32)
+    #mols = [convert_2Dmol_to_data(m) for m in data_mols if m is not None]
+    data_mols = [m for m in data_mols if m is not None]
+    mols = [m for m in tqdm.tqdm(pool.imap_unordered(convert_2Dmol_to_data, data_mols))]
+    mols = [m for m in mols if m is not None]
+    
+    return [{'atoms': m['atom_types'], 
+            'coordinates': m['coords'], 
+            'smi': m['smi'],
+            'mol': ligand,
+            'pocket_name': pocket_name,
+            'pocket_index': pocket_index,
+            'activity': activity, 
+            "pocket_atom_type": pocket_atom_type, 
+            "pocket_coord": pocket_coord} for m in mols]
+
+def mol_parser(mol_path, ligand_path, label):
+    data_mols = read_smi_mol(mol_path)
+    data_mols = [m for m in data_mols if m is not None]  
+    ligand = read_mol2_ligand(ligand_path)
+    pool = mp.Pool(32) 
+    mols = [m for m in tqdm.tqdm(pool.imap_unordered(convert_2Dmol_to_data, data_mols))]
+    mols = [m for m in mols if m is not None]
+    return [{'atoms': m['atom_types'], 
+            'coordinates': m['coords'], 
+            'smi': m['smi'],
+            'mol': m['mol'],
+            'label': label
+            } for m in mols]
+
+def pocket_parser(protein_path, ligand_path, pocket_index, raid=6):
+    protein = read_pdb(protein_path)
+    ligand = read_mol2_ligand(ligand_path)
+    pocket_residue = get_different_raid(protein, ligand, raid=raid)
+    pocket_atom_idx = [i for i, r in enumerate(protein['residue_name']) if r in pocket_residue]
+    pocket_atom_type = [protein['atom_type'][i] for i in pocket_atom_idx]
+    pocket_coord = [protein['coord'][i] for i in pocket_atom_idx]
+    pocket_residue_type = [protein['residue_type'][i] for i in pocket_atom_idx]
+    pocket_name = protein_path.split('/')[-2]
+    return {'pocket': pocket_name,
+            'pocket_index': pocket_index,
+            "pocket_atoms": pocket_atom_type, 
+            "pocket_coordinates": pocket_coord}
+
+def write_lmdb(data, lmdb_path):
+    #resume
+
+    env = lmdb.open(lmdb_path, subdir=False, readonly=False, lock=False, readahead=False, meminit=False, map_size=1099511627776)
+    num = 0
+    with env.begin(write=True) as txn:
+        for d in data:
+            txn.put(str(num).encode('ascii'), pickle.dumps(d))
+            num += 1
+
+if __name__ == '__main__':
+    protein_path = [os.path.join(args.mol_data_path, x, 'receptor.pdb') for x in os.listdir(args.mol_data_path)]
+    act_mol_path = [os.path.join(args.mol_data_path, x, 'actives_final.ism') for x in os.listdir(args.mol_data_path)]
+    decoy_mol_path = [os.path.join(args.mol_data_path, x, 'decoys_final.ism') for x in os.listdir(args.mol_data_path)]
+    
+    
+    
+    for i, pocket in tqdm.tqdm(enumerate(protein_path)):
+        # acive mols
+        print(i, pocket)
+        data = []
+        d_active = (mol_parser(act_mol_path[i], pocket.replace('receptor.pdb', 'crystal_ligand.mol2'), 1))
+        
+        data.extend(d_active)
+
+        # decoy mols
+        d_decoy = (mol_parser(decoy_mol_path[i], pocket.replace('receptor.pdb', 'crystal_ligand.mol2'), 0))
+        
+        data.extend(d_decoy)
+
+        write_lmdb(data, pocket.replace('receptor.pdb', 'mols.lmdb'))
+
+        # write pocket
+        d = pocket_parser(pocket, pocket.replace('receptor.pdb', 'crystal_ligand.mol2'), i)
+        write_lmdb([d], pocket.replace('receptor.pdb', 'pocket.lmdb'))
+
+        # number of lines in actives_final.smi 
+        with open(act_mol_path[i], 'r') as f:
+            mols_lines = list(f.readlines())
+            print("active", len(d_active), len(mols_lines))
+
+
+       
+        with open(decoy_mol_path[i], 'r') as f:
+            mols_lines = list(f.readlines())
+            print("decoy", len(d_decoy), len(mols_lines))
+        
+

data/py_scripts/write_dude_multi.py

@@ -0,0 +1,237 @@
+import argparse
+import gzip
+import multiprocessing as mp
+import os
+import pickle
+import random
+
+import lmdb
+import numpy as np
+import pandas as pd
+import rdkit
+import rdkit.Chem.AllChem as AllChem
+import torch
+import tqdm
+from biopandas.mol2 import PandasMol2
+from biopandas.pdb import PandasPdb
+from rdkit import Chem, RDLogger
+from rdkit.Chem.MolStandardize import rdMolStandardize
+
+RDLogger.DisableLog('rdApp.*')
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--mol_data_path', type=str, default='/HOME/nsccgz_ywang/nsccgz_ywang_wzh/HDD_POOL/qiaoaj/DrugCLIP-Assamble/motivation_analysis_6/data')
+parser.add_argument('--lmdb_path', type=str, default='/HOME/nsccgz_ywang/nsccgz_ywang_wzh/HDD_POOL/qiaoaj/DrugCLIP-Assamble/motivation_analysis_6/data/ood_case.lmdb')
+args = parser.parse_args()
+
+
+def gen_conformation(mol, num_conf=20, num_worker=8):
+    try:
+        mol = Chem.AddHs(mol)
+        AllChem.EmbedMultipleConfs(mol, numConfs=num_conf, numThreads=num_worker, pruneRmsThresh=1, maxAttempts=10000, useRandomCoords=False)
+        try:
+            AllChem.MMFFOptimizeMoleculeConfs(mol, numThreads=num_worker)
+        except:
+            pass
+        mol = Chem.RemoveHs(mol)
+    except:
+        print("cannot gen conf", Chem.MolToSmiles(mol))
+        return None
+    if mol.GetNumConformers() == 0:
+        print("cannot gen conf", Chem.MolToSmiles(mol))
+        return None
+    return mol
+
+def convert_2Dmol_to_data(mol, num_conf=5, num_worker=5):
+    #to 3D
+    mol = gen_conformation(mol, num_conf, num_worker)
+    if mol is None:
+        return None
+    coords = [np.array(mol.GetConformer(i).GetPositions()) for i in range(mol.GetNumConformers())]
+    atom_types = [a.GetSymbol() for a in mol.GetAtoms()]
+    return {'coords': coords, 'atom_types': atom_types, 'smi': Chem.MolToSmiles(mol), 'mol': mol}
+
+def convert_3Dmol_to_data(mol):
+
+    if mol is None:
+        return None
+    coords = [np.array(mol.GetConformer(i).GetPositions()) for i in range(mol.GetNumConformers())]
+    atom_types = [a.GetSymbol() for a in mol.GetAtoms()]
+    return {'coords': coords, 'atom_types': atom_types, 'smi': Chem.MolToSmiles(mol), 'mol': mol}
+
+def read_pdb(path):
+    pdb_df = PandasPdb().read_pdb(path)
+
+    coord = pdb_df.df['ATOM'][['x_coord', 'y_coord', 'z_coord']]
+    atom_type = pdb_df.df['ATOM']['atom_name']
+    residue_name = pdb_df.df['ATOM']['chain_id'] + pdb_df.df['ATOM']['residue_number'].astype(str)
+    residue_type = pdb_df.df['ATOM']['residue_name']
+    protein = {'coord': np.array(coord), 
+               'atom_type': list(atom_type),
+               'residue_name': list(residue_name),
+               'residue_type': list(residue_type)}
+    return protein
+
+
+def read_sdf_gz_3d(path):
+    inf = gzip.open(path)
+    with Chem.ForwardSDMolSupplier(inf, removeHs=False, sanitize=False) as gzsuppl:
+        ms = [add_charges(x) for x in gzsuppl if x is not None]
+    ms = [rdMolStandardize.Uncharger().uncharge(Chem.RemoveHs(m)) for m in ms if m is not None]
+    return ms
+
+def add_charges(m):
+    m.UpdatePropertyCache(strict=False)
+    ps = Chem.DetectChemistryProblems(m)
+    if not ps:
+        Chem.SanitizeMol(m)
+        return m
+    for p in ps:
+        if p.GetType()=='AtomValenceException':
+            at = m.GetAtomWithIdx(p.GetAtomIdx())
+            if at.GetAtomicNum()==7 and at.GetFormalCharge()==0 and at.GetExplicitValence()==4:
+                at.SetFormalCharge(1)
+            if at.GetAtomicNum()==6 and at.GetExplicitValence()==5:
+                #remove a bond
+                for b in at.GetBonds():
+                    if b.GetBondType()==Chem.rdchem.BondType.DOUBLE:
+                        b.SetBondType(Chem.rdchem.BondType.SINGLE)
+                        break
+            if at.GetAtomicNum()==8 and at.GetFormalCharge()==0 and at.GetExplicitValence()==3:
+                at.SetFormalCharge(1)
+            if at.GetAtomicNum()==5 and at.GetFormalCharge()==0 and at.GetExplicitValence()==4:
+                at.SetFormalCharge(-1)
+    try:
+        Chem.SanitizeMol(m)
+    except:
+        return None
+    return m
+
+def get_different_raid(protein, ligand, raid=6):
+    protein_coord = protein['coord']
+    ligand_coord = ligand['coord']
+    protein_residue_name = protein['residue_name']
+    pocket_residue = set()
+    for i in range(len(protein_coord)):
+        for j in range(len(ligand_coord)):
+            if np.linalg.norm(protein_coord[i] - ligand_coord[j]) < raid:
+                pocket_residue.add(protein_residue_name[i])
+    return pocket_residue
+
+def read_mol2_ligand(path):
+    mol2_df = PandasMol2().read_mol2(path)
+    coord = mol2_df.df[['x', 'y', 'z']]
+    atom_type = mol2_df.df['atom_name']
+    ligand = {'coord': np.array(coord), 'atom_type': list(atom_type), 'mol': Chem.MolFromMol2File(path)}
+    return ligand
+
+def read_smi_mol(path):
+    with open(path, 'r') as f:
+        mols_lines = list(f.readlines())
+    smis = [l.split(' ')[0] for l in mols_lines]
+    mols = [Chem.MolFromSmiles(m) for m in smis]
+    return mols
+
+def parser(protein_path, mol_path, ligand_path, activity, pocket_index, raid=6):
+    protein = read_pdb(protein_path)
+    data_mols = read_smi_mol(mol_path)
+
+    ligand = read_mol2_ligand(ligand_path)
+    pocket_residue = get_different_raid(protein, ligand, raid=raid)
+    pocket_atom_idx = [i for i, r in enumerate(protein['residue_name']) if r in pocket_residue]
+    pocket_atom_type = [protein['atom_type'][i] for i in pocket_atom_idx]
+    pocket_coord = [protein['coord'][i] for i in pocket_atom_idx]
+    pocket_residue_type = [protein['residue_type'][i] for i in pocket_atom_idx]
+    pocket_name = protein_path.split('/')[-2]
+    pool = mp.Pool(6)
+    #mols = [convert_2Dmol_to_data(m) for m in data_mols if m is not None]
+    data_mols = [m for m in data_mols if m is not None]
+    mols = [m for m in tqdm.tqdm(pool.imap_unordered(convert_2Dmol_to_data, data_mols))]
+    mols = [m for m in mols if m is not None]
+    
+    return [{'atoms': m['atom_types'], 
+            'coordinates': m['coords'], 
+            'smi': m['smi'],
+            'mol': ligand,
+            'pocket_name': pocket_name,
+            'pocket_index': pocket_index,
+            'activity': activity, 
+            "pocket_atom_type": pocket_atom_type, 
+            "pocket_coord": pocket_coord} for m in mols]
+
+def mol_parser(mol_path, ligand_path, label):
+    data_mols = read_smi_mol(mol_path)
+    data_mols = [m for m in data_mols if m is not None]  
+    ligand = read_mol2_ligand(ligand_path)
+    pool = mp.Pool(32) 
+    mols = [m for m in tqdm.tqdm(pool.imap_unordered(convert_2Dmol_to_data, data_mols))]
+    mols = [m for m in mols if m is not None]
+    return [{'atoms': m['atom_types'], 
+            'coordinates': m['coords'], 
+            'smi': m['smi'],
+            'mol': m['mol'],
+            'label': label
+            } for m in mols]
+
+def pocket_parser(protein_path, ligand_path, pocket_index, raid=6):
+    protein = read_pdb(protein_path)
+    ligand = read_mol2_ligand(ligand_path)
+    pocket_residue = get_different_raid(protein, ligand, raid=raid)
+    pocket_atom_idx = [i for i, r in enumerate(protein['residue_name']) if r in pocket_residue]
+    pocket_atom_type = [protein['atom_type'][i] for i in pocket_atom_idx]
+    pocket_coord = [protein['coord'][i] for i in pocket_atom_idx]
+    pocket_residue_type = [protein['residue_type'][i] for i in pocket_atom_idx]
+    pocket_name = protein_path.split('/')[-2]
+    return {'pocket': pocket_name,
+            'pocket_index': pocket_index,
+            "pocket_atoms": pocket_atom_type, 
+            "pocket_coordinates": pocket_coord}
+
+def write_lmdb(data, lmdb_path):
+    #resume
+
+    env = lmdb.open(lmdb_path, subdir=False, readonly=False, lock=False, readahead=False, meminit=False, map_size=1099511627776)
+    num = 0
+    with env.begin(write=True) as txn:
+        for d in data:
+            txn.put(str(num).encode('ascii'), pickle.dumps(d))
+            num += 1
+
+if __name__ == '__main__':
+    protein_path = [os.path.join(args.mol_data_path, x, 'receptor.pdb') for x in os.listdir(args.mol_data_path)]
+    act_mol_path = [os.path.join(args.mol_data_path, x, 'actives_final.ism') for x in os.listdir(args.mol_data_path)]
+    decoy_mol_path = [os.path.join(args.mol_data_path, x, 'decoys_final.ism') for x in os.listdir(args.mol_data_path)]
+    
+    
+    
+    for i, pocket in tqdm.tqdm(enumerate(protein_path)):
+        # acive mols
+        print(i, pocket)
+        data = []
+        d_active = (mol_parser(act_mol_path[i], pocket.replace('receptor.pdb', 'crystal_ligand.mol2'), 1))
+        
+        data.extend(d_active)
+
+        # decoy mols
+        d_decoy = (mol_parser(decoy_mol_path[i], pocket.replace('receptor.pdb', 'crystal_ligand.mol2'), 0))
+        
+        data.extend(d_decoy)
+
+        write_lmdb(data, pocket.replace('receptor.pdb', 'mols.lmdb'))
+
+        # write pocket
+        d = pocket_parser(pocket, pocket.replace('receptor.pdb', 'crystal_ligand.mol2'), i)
+        write_lmdb([d], pocket.replace('receptor.pdb', 'pocket.lmdb'))
+
+        # number of lines in actives_final.smi 
+        with open(act_mol_path[i], 'r') as f:
+            mols_lines = list(f.readlines())
+            print("active", len(d_active), len(mols_lines))
+
+
+       
+        with open(decoy_mol_path[i], 'r') as f:
+            mols_lines = list(f.readlines())
+            print("decoy", len(d_decoy), len(mols_lines))
+        
+

data/retrieval.sh

@@ -0,0 +1,18 @@
+results_path="./test"  # replace to your results path
+batch_size=8
+weight_path="ckpt/savedirrandom_zinc/checkpoint_best_seed-2025.pt"
+MOL_PATH="retrieval/mols.lmdb" # path to the molecule file
+POCKET_PATH="retrieval/pocket.lmdb" # path to the pocket file
+EMB_DIR="retrieval/emb" # path to the cached mol embedding file
+
+CUDA_VISIBLE_DEVICES="1" python ./unimol/retrieval.py --user-dir ./unimol $data_path "retrieval" --valid-subset test \
+       --results-path $results_path \
+       --num-workers 8 --ddp-backend=c10d --batch-size $batch_size \
+       --task drugclip --loss in_batch_softmax --arch drugclip  \
+       --max-pocket-atoms 256 \
+       --fp16 --fp16-init-scale 4 --fp16-scale-window 256  --seed 1 \
+       --path $weight_path \
+       --log-interval 100 --log-format simple \
+       --mol-path $MOL_PATH \
+       --pocket-path $POCKET_PATH \
+       --emb-dir $EMB_DIR \

data/test.sh

@@ -0,0 +1,15 @@
+results_path="./test"  # replace to your results path
+batch_size=8
+weight_path="checkpoint_best_seed.pt"
+
+TASK="DUDE" # DUDE or PCBA
+
+CUDA_LAUNCH_BLOCKING=1 PYTHONFAULTHANDLER=1  CUDA_VISIBLE_DEVICES="1" python ./unimol/test.py --user-dir ./unimol $data_path "./data" --valid-subset test \
+       --results-path $results_path \
+       --num-workers 0 --ddp-backend=c10d --batch-size $batch_size \
+       --task drugclip --loss in_batch_softmax --arch drugclip  \
+       --fp16 --fp16-init-scale 4 --fp16-scale-window 256  --seed 1 \
+       --path $weight_path \
+       --log-interval 100 --log-format simple \
+       --max-pocket-atoms 511 \
+       --test-task $TASK \

data/unimol/__init__.py

@@ -0,0 +1,6 @@
+import importlib
+import unimol.tasks
+import unimol.data
+import unimol.models
+import unimol.losses
+import unimol.utils

data/unimol/data/__init__.py

@@ -0,0 +1,51 @@
+from .key_dataset import KeyDataset, LengthDataset
+from .normalize_dataset import (
+    NormalizeDataset,
+    NormalizeDockingPoseDataset,
+)
+from .remove_hydrogen_dataset import (
+    RemoveHydrogenDataset,
+    DockRemoveHydrogenDataset, ##
+    RemoveHydrogenResiduePocketDataset,
+    RemoveHydrogenPocketDataset,
+)
+from .tta_dataset import (
+    TTADataset,
+    TTADecoderDataset,
+    TTADockingPoseDataset,
+)
+from .cropping_dataset import (
+    CroppingDataset,
+    CroppingPocketDataset,
+    CroppingResiduePocketDataset,
+    CroppingPocketDockingPoseDataset,
+    CroppingPocketDockingPoseTestDataset,
+)
+from .atom_type_dataset import AtomTypeDataset
+from .add_2d_conformer_dataset import Add2DConformerDataset
+from .distance_dataset import (
+    DistanceDataset,
+    EdgeTypeDataset,
+    CrossDistanceDataset,
+    CrossEdgeTypeDataset
+)
+from .conformer_sample_dataset import (
+    ConformerSampleDataset,
+    ConformerSampleDecoderDataset,
+    ConformerSamplePocketDataset,
+    ConformerSamplePocketFinetuneDataset,
+    ConformerSampleConfGDataset,
+    ConformerSampleConfGV2Dataset,
+    ConformerSampleDockingPoseDataset,
+)
+from .mask_points_dataset import MaskPointsDataset, MaskPointsPocketDataset
+from .coord_pad_dataset import RightPadDatasetCoord, RightPadDatasetCross2D
+from .from_str_dataset import FromStrLabelDataset
+from .lmdb_dataset import LMDBDataset
+from .prepend_and_append_2d_dataset import PrependAndAppend2DDataset
+from .affinity_dataset import AffinityDataset, AffinityTestDataset, AffinityValidDataset, AffinityMolDataset, AffinityPocketDataset, AffinityHNSDataset, AffinityAugDataset
+from .pocket2mol_dataset import FragmentConformationDataset
+from .vae_binding_dataset import VAEBindingDataset, VAEBindingTestDataset, VAEGenerationTestDataset
+from .resampling_dataset import ResamplingDataset
+from .complex_center_dataset import ComplexCenterDataset
+__all__ = []

data/unimol/data/add_2d_conformer_dataset.py

@@ -0,0 +1,46 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+from rdkit import Chem
+from rdkit.Chem import AllChem
+
+
+class Add2DConformerDataset(BaseWrapperDataset):
+    def __init__(self, dataset, smi, atoms, coordinates):
+        self.dataset = dataset
+        self.smi = smi
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        assert len(atoms) > 0
+        smi = self.dataset[index][self.smi]
+        coordinates_2d = smi2_2Dcoords(smi)
+        coordinates = self.dataset[index][self.coordinates]
+        coordinates.append(coordinates_2d)
+        return {"smi": smi, "atoms": atoms, "coordinates": coordinates}
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+def smi2_2Dcoords(smi):
+    mol = Chem.MolFromSmiles(smi)
+    mol = AllChem.AddHs(mol)
+    AllChem.Compute2DCoords(mol)
+    coordinates = mol.GetConformer().GetPositions().astype(np.float32)
+    len(mol.GetAtoms()) == len(
+        coordinates
+    ), "2D coordinates shape is not align with {}".format(smi)
+    return coordinates

data/unimol/data/affinity_dataset.py

@@ -0,0 +1,567 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+from functools import lru_cache
+
+import numpy as np
+from unicore.data import BaseWrapperDataset
+
+from . import data_utils
+
+
+class AffinityDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        decoy_atoms,
+        decoy_coordinates,
+        docked_coordinates,
+        pocket_atoms,
+        pocket_coordinates,
+        affinity,
+        is_train=False,
+        pocket="pocket"
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.decoy_atoms = decoy_atoms
+        self.decoy_coordinates = decoy_coordinates
+        self.docked_coordinates = docked_coordinates
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.affinity = affinity
+        self.is_train = is_train
+        self.pocket=pocket
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        ori_mol_length = len(atoms)
+        size = len(self.dataset[index][self.coordinates])
+        if self.is_train:
+            with data_utils.numpy_seed(self.seed, epoch, index):
+                sample_idx = np.random.randint(size)
+        else:
+            with data_utils.numpy_seed(self.seed, 1, index):
+                sample_idx = np.random.randint(size)
+        coordinates = self.dataset[index][self.coordinates][sample_idx]
+
+        # docking  
+        docked_coordinates = self.dataset[index][self.docked_coordinates]
+        # # fingerprints
+        scaffold_fp = self.dataset[index]["scaffold_fp"]
+        property_vec = self.dataset[index]["scaffold_fp"]
+
+        # # decoy using pool
+        # item = self.dataset[index]
+        # pool_atoms = item["hard_decoy_atoms"]            # list, len = 10
+        # pool_coords = item["hard_decoy_coordinates"]     # list of conf-list
+        # pool_fp    = item["hard_decoy_scaffold_fp"]  # optional list
+        # pool_size = len(pool_atoms)
+        # if self.is_train:
+        #     with data_utils.numpy_seed(self.seed, epoch, index):
+        #         pool_idx = np.random.randint(pool_size)
+        # else:
+        #     with data_utils.numpy_seed(self.seed, 1, index):
+        #         pool_idx = np.random.randint(pool_size)
+        # decoy_atoms = np.array(pool_atoms[pool_idx])
+        # decoy_num_conf = len(pool_coords[pool_idx])
+        # if self.is_train:
+        #     with data_utils.numpy_seed(self.seed, epoch, index, pool_idx):
+        #         decoy_conf_idx = np.random.randint(decoy_num_conf)
+        # else:
+        #     with data_utils.numpy_seed(self.seed, 1, index, pool_idx):
+        #         decoy_conf_idx = np.random.randint(decoy_num_conf)
+
+        # decoy_coordinates = pool_coords[pool_idx][decoy_conf_idx]
+        # decoy_scaffold_fp = pool_fp[pool_idx]
+        # decoy_property_vec = decoy_scaffold_fp
+        
+        decoy_atoms = np.array(self.dataset[index][self.decoy_atoms])
+        decoy_num_conf = len(self.dataset[index][self.decoy_coordinates])
+        if self.is_train:
+            with data_utils.numpy_seed(self.seed, epoch, index):
+                decoy_conf_idx = np.random.randint(decoy_num_conf)
+        else:
+            with data_utils.numpy_seed(self.seed, 1, index):
+                decoy_conf_idx = np.random.randint(decoy_num_conf)
+        decoy_coordinates = self.dataset[index][self.decoy_coordinates][decoy_conf_idx]
+        decoy_scaffold_fp = self.dataset[index]["hard_decoy_scaffold_fp"]
+        decoy_property_vec = self.dataset[index]["hard_decoy_scaffold_fp"]
+        # decoy_scaffold_fp = self.dataset[index]["decoy_scaffold_fp"]
+        # decoy_property_vec = self.dataset[index]["decoy_scaffold_fp"]
+
+        pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index][self.pocket_atoms]]
+        )
+        ori_pocket_length = len(pocket_atoms)
+        pocket_coordinates = np.stack(self.dataset[index][self.pocket_coordinates])
+
+        smi = self.dataset[index]["smi"]
+        hard_label = self.dataset[index]["hard_is_random"]
+        pocket = self.dataset[index][self.pocket]
+        if self.affinity in self.dataset[index]:
+            affinity = float(self.dataset[index][self.affinity])
+        else:
+            affinity = 1
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+
+            "decoy_atoms": decoy_atoms,
+            "decoy_coordinates": np.array(decoy_coordinates, dtype=np.float32),
+            "docked_coordinates": docked_coordinates.astype(np.float32),
+            "scaffold_fp": scaffold_fp,
+            "decoy_scaffold_fp": decoy_scaffold_fp,
+            "property_vec": property_vec,
+            "decoy_property_vec": decoy_property_vec,
+            'hard_label':hard_label,
+
+            "holo_coordinates": coordinates.astype(np.float32),#placeholder
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": pocket_coordinates.astype(np.float32),#placeholder
+            "smi": smi,
+            "pocket": pocket,
+            "affinity": affinity,
+            "ori_mol_length": ori_mol_length,
+            "ori_pocket_length": ori_pocket_length
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class AffinityAugDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        pocket_atoms,
+        pocket_coordinates,
+        affinity,
+        is_train=False,
+        pocket="pocket_id"
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.affinity = affinity
+        self.is_train = is_train
+        self.pocket=pocket
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        #mol_atoms_list = self.dataset[index][self.atoms]
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            mol_idx = np.random.randint(len(self.dataset[index][self.atoms]))
+        atoms = np.array(self.dataset[index][self.atoms][mol_idx])
+        ori_mol_length = len(atoms)
+        #coordinates = self.dataset[index][self.coordinates]
+        size = len(self.dataset[index][self.coordinates][mol_idx])
+        if self.is_train:
+            with data_utils.numpy_seed(self.seed, epoch, index):
+                sample_idx = np.random.randint(size)
+        else:
+            with data_utils.numpy_seed(self.seed, 1, index):
+                sample_idx = np.random.randint(size)
+        #print(len(self.dataset[index][self.coordinates][sample_idx]))
+        coordinates = self.dataset[index][self.coordinates][mol_idx][sample_idx]
+
+
+        #pocket_list = self.dataset[index][self.pocket_atoms]
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            pocket_idx = np.random.randint(len(self.dataset[index][self.pocket_atoms]))
+        pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index][self.pocket_atoms][pocket_idx]]
+        )
+
+        ori_pocket_length = len(pocket_atoms)
+        pocket_coordinates = np.stack(self.dataset[index][self.pocket_coordinates][pocket_idx])
+
+        smi = self.dataset[index]["smiles"][mol_idx]
+        pocket = self.dataset[index][self.pocket][0]
+        if self.affinity in self.dataset[index]:
+            affinity = float(self.dataset[index][self.affinity])
+        else:
+            affinity = 1
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "holo_coordinates": coordinates.astype(np.float32),#placeholder
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": pocket_coordinates.astype(np.float32),#placeholder
+            "smi": smi,
+            "pocket": pocket,
+            "affinity": affinity,
+            "ori_mol_length": ori_mol_length,
+            "ori_pocket_length": ori_pocket_length
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class AffinityHNSDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        atoms_hns,
+        coordinates_hns,
+        pocket_atoms,
+        pocket_coordinates,
+        affinity,
+        is_train=False,
+        pocket="pocket"
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.atoms_hns = atoms_hns
+        self.coordinates_hns = coordinates_hns
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.affinity = affinity
+        self.is_train = is_train
+        self.pocket=pocket
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        ori_mol_length = len(atoms)
+        #coordinates = self.dataset[index][self.coordinates]
+        size = len(self.dataset[index][self.coordinates])
+        if self.is_train:
+            with data_utils.numpy_seed(self.seed, epoch, index):
+                sample_idx = np.random.randint(size)
+        else:
+            with data_utils.numpy_seed(self.seed, 1, index):
+                sample_idx = np.random.randint(size)
+        #print(len(self.dataset[index][self.coordinates][sample_idx]))
+        coordinates = self.dataset[index][self.coordinates][sample_idx]
+        atoms_hns = np.array(self.dataset[index][self.atoms_hns])
+        coordinates_hns = self.dataset[index][self.coordinates_hns][0]
+
+
+
+        pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index][self.pocket_atoms]]
+        )
+        ori_pocket_length = len(pocket_atoms)
+        pocket_coordinates = np.stack(self.dataset[index][self.pocket_coordinates])
+
+        smi = self.dataset[index]["smi"]
+        pocket = self.dataset[index][self.pocket]
+        if self.affinity in self.dataset[index]:
+            affinity = float(self.dataset[index][self.affinity])
+        else:
+            affinity = 1
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "atoms_hns": atoms_hns,
+            "coordinates_hns": coordinates_hns.astype(np.float32),
+            "holo_coordinates": coordinates.astype(np.float32),#placeholder
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": pocket_coordinates.astype(np.float32),#placeholder
+            "smi": smi,
+            "pocket": pocket,
+            "affinity": affinity,
+            "ori_mol_length": ori_mol_length,
+            "ori_pocket_length": ori_pocket_length
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+class AffinityTestDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        pocket_atoms,
+        pocket_coordinates,
+        affinity=None,
+        is_train=False,
+        pocket="pocket"
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.affinity = affinity
+        self.is_train = is_train
+        self.pocket=pocket
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        ori_length = len(atoms)
+        #coordinates = self.dataset[index][self.coordinates]
+        size = len(self.dataset[index][self.coordinates])
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            sample_idx = np.random.randint(size)
+        coordinates = self.dataset[index][self.coordinates][sample_idx]
+        pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index][self.pocket_atoms]]
+        )
+        #print(len(self.dataset[index][self.pocket_coordinates]))
+        pocket_coordinates = np.stack(self.dataset[index][self.pocket_coordinates])
+
+        smi = self.dataset[index]["smi"]
+        pocket = self.dataset[index][self.pocket]
+        affinity = self.dataset[index][self.affinity]
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "holo_coordinates": coordinates.astype(np.float32),#placeholder
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": pocket_coordinates.astype(np.float32),#placeholder
+            "smi": smi,
+            "pocket": pocket,
+            "affinity": affinity.astype(np.float32),
+            "ori_length": ori_length
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class AffinityMolDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        is_train=False,
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.is_train = is_train
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        #print(self.dataset[index])
+        atoms = np.array(self.dataset[index][self.atoms])
+        ori_length = len(atoms)
+        #coordinates = self.dataset[index][self.coordinates]
+        size = len(self.dataset[index][self.coordinates])
+        #print(size)
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            sample_idx = np.random.randint(size)
+        # check coordinates is 2 dimension or not
+        if len(self.dataset[index][self.coordinates][sample_idx].shape) == 2:
+            coordinates = self.dataset[index][self.coordinates][sample_idx]
+        else:
+            coordinates = self.dataset[index][self.coordinates]
+        #coordinates = self.dataset[index][self.coordinates][sample_idx]
+        #coordinates = self.dataset[index][self.coordinates]
+        
+        smi = self.dataset[index]["smi"]
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "holo_coordinates": coordinates.astype(np.float32),#placeholder
+            "smi": smi,
+            "ori_length": ori_length
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class AffinityPocketDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        pocket_atoms,
+        pocket_coordinates,
+        is_train=False,
+        pocket="pocket"
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.is_train = is_train
+        self.pocket=pocket
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index][self.pocket_atoms]]
+        )
+        ori_length = len(pocket_atoms)
+        pocket_coordinates = np.stack(self.dataset[index][self.pocket_coordinates])
+        if self.pocket in self.dataset[index]:
+            pocket = self.dataset[index][self.pocket]
+        else:
+            pocket = ""
+        return {
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": pocket_coordinates.astype(np.float32),#placeholder
+            "pocket": pocket,
+            "ori_length": ori_length
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+class AffinityValidDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        pocket_atoms,
+        pocket_coordinates,
+        pocket="pocket"
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.pocket=pocket
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        ori_mol_length = len(atoms)
+        #coordinates = self.dataset[index][self.coordinates]
+
+        size = len(self.dataset[index][self.coordinates])
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            sample_idx = np.random.randint(size)
+        coordinates = self.dataset[index][self.coordinates][sample_idx]
+        pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index][self.pocket_atoms]]
+        )
+        ori_pocket_length = len(pocket_atoms)
+        pocket_coordinates = np.stack(self.dataset[index][self.pocket_coordinates])
+
+        smi = self.dataset[index]["smi"]
+        pocket = self.dataset[index][self.pocket]
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "coordinates": coordinates.astype(np.float32),
+            "holo_coordinates": coordinates.astype(np.float32),#placeholder
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": pocket_coordinates.astype(np.float32),#placeholder
+            "smi": smi,
+            "pocket": pocket,
+            "ori_mol_length": ori_mol_length,
+            "ori_pocket_length": ori_pocket_length
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)

data/unimol/data/atom_type_dataset.py

@@ -0,0 +1,34 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+
+
+class AtomTypeDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        raw_dataset,
+        dataset,
+        smi="smi",
+        atoms="atoms",
+    ):
+        self.raw_dataset = raw_dataset
+        self.dataset = dataset
+        self.smi = smi
+        self.atoms = atoms
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, index: int):
+        # for low rdkit version
+        if len(self.dataset[index]["atoms"]) != len(self.dataset[index]["coordinates"]):
+            min_len = min(
+                len(self.dataset[index]["atoms"]),
+                len(self.dataset[index]["coordinates"]),
+            )
+            self.dataset[index]["atoms"] = self.dataset[index]["atoms"][:min_len]
+            self.dataset[index]["coordinates"] = self.dataset[index]["coordinates"][
+                :min_len
+            ]
+        return self.dataset[index]

data/unimol/data/complex_center_dataset.py

@@ -0,0 +1,65 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+
+
+class ComplexCenterDataset(BaseWrapperDataset):
+    """     
+    compute COM for duffsion
+    """
+
+    def __init__(
+        self,
+        dataset,
+        lig_coord_key="coordinates",
+        pocket_coord_key="pocket_coordinates",
+        out_lig_key="lig_coord_com",
+        out_pocket_key="pocket_coord_com",
+    ):
+        self.dataset = dataset
+        self.lig_coord_key = lig_coord_key
+        self.pocket_coord_key = pocket_coord_key
+        self.out_lig_key = out_lig_key
+        self.out_pocket_key = out_pocket_key
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+
+        dd = self.dataset[index].copy()
+
+        lig = dd[self.lig_coord_key]        # (N_lig, 3)
+        poc = dd[self.pocket_coord_key]     # (N_poc, 3)
+
+
+        # if lig.size == 0 and poc.size == 0:
+        #     return dd
+
+        # if lig.size == 0:
+        #     all_pos = poc
+        # elif poc.size == 0:
+        #     all_pos = lig
+        # else:
+        #     all_pos = np.concatenate([lig, poc], axis=0)   # (N_lig+N_poc, 3)
+        #using pocket coordinates to compute COM
+        all_pos = poc
+        center = all_pos.mean(axis=0, keepdims=True)       # (1, 3)
+
+        lig_centered = lig - center
+        poc_centered = poc - center
+
+        dd[self.out_lig_key] = lig_centered.astype(np.float32)
+        dd[self.out_pocket_key] = poc_centered.astype(np.float32)
+
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)

data/unimol/data/conformer_sample_dataset.py

@@ -0,0 +1,315 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+from . import data_utils
+
+
+class ConformerSampleDataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, coordinates):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        #print(index,self.dataset[index])
+        atoms = np.array(self.dataset[index][self.atoms])
+        assert len(atoms) > 0
+        size = len(self.dataset[index][self.coordinates])
+        #print(size)
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            sample_idx = np.random.randint(size)
+        coordinates = self.dataset[index][self.coordinates][sample_idx]
+        return {"atoms": atoms, "coordinates": coordinates.astype(np.float32)}
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+class ConformerSampleDecoderDataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, coordinates, selfies):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.selfies = selfies
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        assert len(atoms) > 0
+        # print("self.dataset[index][self.atoms]")
+        # print(self.dataset[index][self.atoms])        
+        # print("self.dataset[index][self.selfies]")
+        # print(self.dataset[index][self.selfies])
+        selfies = np.array(self.dataset[index][self.selfies])
+        assert len(selfies) > 0
+        size = len(self.dataset[index][self.coordinates])
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            sample_idx = np.random.randint(size)
+        coordinates = self.dataset[index][self.coordinates][sample_idx]
+        return {"atoms": atoms, "selfies": selfies, "coordinates": coordinates.astype(np.float32)}
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+    
+class ConformerSamplePocketDataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, coordinates, dict_name):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.dict_name = dict_name
+        self.coordinates = coordinates
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        if self.dict_name == "dict_coarse.txt":
+            atoms = np.array([a[0] for a in self.dataset[index][self.atoms]])
+        elif self.dict_name == "dict_fine.txt":
+            atoms = np.array(
+                [
+                    a[0] if len(a) == 1 or a[0] == "H" else a[:2]
+                    for a in self.dataset[index][self.atoms]
+                ]
+            )
+        assert len(atoms) > 0
+        size = len(self.dataset[index][self.coordinates])
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            sample_idx = np.random.randint(size)
+        coordinates = self.dataset[index][self.coordinates][sample_idx]
+        residue = np.array(self.dataset[index]["residue"])
+        score = np.float(self.dataset[index]["meta_info"]["fpocket"]["Score"])
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "residue": residue,
+            "score": score,
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class ConformerSamplePocketFinetuneDataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, residues, coordinates):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.residues = residues
+        self.coordinates = coordinates
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(
+            [a[0] for a in self.dataset[index][self.atoms]]
+        )  # only 'C H O N S'
+        assert len(atoms) > 0
+        # This judgment is reserved for possible future expansion.
+        # The number of pocket conformations is 1, and the 'sample' does not work.
+        if isinstance(self.dataset[index][self.coordinates], list):
+            size = len(self.dataset[index][self.coordinates])
+            with data_utils.numpy_seed(self.seed, epoch, index):
+                sample_idx = np.random.randint(size)
+            coordinates = self.dataset[index][self.coordinates][sample_idx]
+        else:
+            coordinates = self.dataset[index][self.coordinates]
+
+        if self.residues in self.dataset[index]:
+            residues = np.array(self.dataset[index][self.residues])
+        else:
+            residues = None
+        assert len(atoms) == len(coordinates)
+        return {
+            self.atoms: atoms,
+            self.coordinates: coordinates.astype(np.float32),
+            self.residues: residues,
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class ConformerSampleConfGDataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, coordinates, tgt_coordinates):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.tgt_coordinates = tgt_coordinates
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        assert len(atoms) > 0
+        size = len(self.dataset[index][self.coordinates])
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            sample_idx = np.random.randint(size)
+        coordinates = self.dataset[index][self.coordinates][sample_idx]
+        tgt_coordinates = self.dataset[index][self.tgt_coordinates]
+        return {
+            self.atoms: atoms,
+            self.coordinates: coordinates.astype(np.float32),
+            self.tgt_coordinates: tgt_coordinates.astype(np.float32),
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class ConformerSampleConfGV2Dataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        tgt_coordinates,
+        beta=1.0,
+        smooth=0.1,
+        topN=10,
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.tgt_coordinates = tgt_coordinates
+        self.beta = beta
+        self.smooth = smooth
+        self.topN = topN
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        assert len(atoms) > 0
+        meta_df = self.dataset[index]["meta"]
+        tgt_conf_ids = meta_df["gid"].unique()
+        # randomly choose one conf
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            conf_id = np.random.choice(tgt_conf_ids)
+        conf_df = meta_df[meta_df["gid"] == conf_id]
+        conf_df = conf_df.sort_values("score").reset_index(drop=False)[
+            : self.topN
+        ]  # only use top 5 confs for sampling...
+        # importance sampling with rmsd inverse score
+
+        def normalize(x, beta=1.0, smooth=0.1):
+            x = 1.0 / (x**beta + smooth)
+            return x / x.sum()
+
+        rmsd_score = conf_df["score"].values
+        weight = normalize(
+            rmsd_score, beta=self.beta, smooth=self.smooth
+        )  # for smoothing purpose
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            idx = np.random.choice(len(conf_df), 1, replace=False, p=weight)
+        # idx = [np.argmax(weight)]
+        coordinates = conf_df.iloc[idx]["rdkit_coords"].values[0]
+        tgt_coordinates = conf_df.iloc[idx]["tgt_coords"].values[0]
+        return {
+            self.atoms: atoms,
+            self.coordinates: coordinates.astype(np.float32),
+            self.tgt_coordinates: tgt_coordinates.astype(np.float32),
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class ConformerSampleDockingPoseDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        pocket_atoms,
+        pocket_coordinates,
+        holo_coordinates,
+        holo_pocket_coordinates,
+        is_train=True,
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.holo_coordinates = holo_coordinates
+        self.holo_pocket_coordinates = holo_pocket_coordinates
+        self.is_train = is_train
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        size = len(self.dataset[index][self.coordinates])
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            sample_idx = np.random.randint(size)
+        coordinates = self.dataset[index][self.coordinates][sample_idx]
+        pocket_atoms = np.array(
+            [item[0] for item in self.dataset[index][self.pocket_atoms]]
+        )
+        pocket_coordinates = self.dataset[index][self.pocket_coordinates][0]
+        if self.is_train:
+            holo_coordinates = self.dataset[index][self.holo_coordinates][0]
+            holo_pocket_coordinates = self.dataset[index][self.holo_pocket_coordinates][
+                0
+            ]
+        else:
+            holo_coordinates = coordinates
+            holo_pocket_coordinates = pocket_coordinates
+
+        smi = self.dataset[index]["smi"]
+        pocket = self.dataset[index]["pocket"]
+
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_coordinates": holo_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": holo_pocket_coordinates.astype(np.float32),
+            "smi": smi,
+            "pocket": pocket,
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)

data/unimol/data/coord_pad_dataset.py

@@ -0,0 +1,82 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from unicore.data import BaseWrapperDataset
+
+
+def collate_tokens_coords(
+    values,
+    pad_idx,
+    left_pad=False,
+    pad_to_length=None,
+    pad_to_multiple=1,
+):
+    """Convert a list of 1d tensors into a padded 2d tensor."""
+    size = max(v.size(0) for v in values)
+    size = size if pad_to_length is None else max(size, pad_to_length)
+    if pad_to_multiple != 1 and size % pad_to_multiple != 0:
+        size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
+    res = values[0].new(len(values), size, 3).fill_(pad_idx)
+
+    def copy_tensor(src, dst):
+        assert dst.numel() == src.numel()
+        dst.copy_(src)
+
+    for i, v in enumerate(values):
+        copy_tensor(v, res[i][size - len(v) :, :] if left_pad else res[i][: len(v), :])
+    return res
+
+
+class RightPadDatasetCoord(BaseWrapperDataset):
+    def __init__(self, dataset, pad_idx, left_pad=False):
+        super().__init__(dataset)
+        self.pad_idx = pad_idx
+        self.left_pad = left_pad
+
+    def collater(self, samples):
+        return collate_tokens_coords(
+            samples, self.pad_idx, left_pad=self.left_pad, pad_to_multiple=8
+        )
+
+
+def collate_cross_2d(
+    values,
+    pad_idx,
+    left_pad=False,
+    pad_to_length=None,
+    pad_to_multiple=1,
+):
+    """Convert a list of 2d tensors into a padded 2d tensor."""
+    size_h = max(v.size(0) for v in values)
+    size_w = max(v.size(1) for v in values)
+    if pad_to_multiple != 1 and size_h % pad_to_multiple != 0:
+        size_h = int(((size_h - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
+    if pad_to_multiple != 1 and size_w % pad_to_multiple != 0:
+        size_w = int(((size_w - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
+    res = values[0].new(len(values), size_h, size_w).fill_(pad_idx)
+
+    def copy_tensor(src, dst):
+        assert dst.numel() == src.numel()
+        dst.copy_(src)
+
+    for i, v in enumerate(values):
+        copy_tensor(
+            v,
+            res[i][size_h - v.size(0) :, size_w - v.size(1) :]
+            if left_pad
+            else res[i][: v.size(0), : v.size(1)],
+        )
+    return res
+
+
+class RightPadDatasetCross2D(BaseWrapperDataset):
+    def __init__(self, dataset, pad_idx, left_pad=False):
+        super().__init__(dataset)
+        self.pad_idx = pad_idx
+        self.left_pad = left_pad
+
+    def collater(self, samples):
+        return collate_cross_2d(
+            samples, self.pad_idx, left_pad=self.left_pad, pad_to_multiple=8
+        )

data/unimol/data/cropping_dataset.py

@@ -0,0 +1,269 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+from functools import lru_cache
+import logging
+from unicore.data import BaseWrapperDataset
+from . import data_utils
+
+logger = logging.getLogger(__name__)
+
+
+class CroppingDataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, coordinates, max_atoms=256):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.max_atoms = max_atoms
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        atoms = dd[self.atoms]
+        coordinates = dd[self.coordinates]
+        if self.max_atoms and len(atoms) > self.max_atoms:
+            with data_utils.numpy_seed(self.seed, epoch, index):
+                index = np.random.choice(len(atoms), self.max_atoms, replace=False)
+                atoms = np.array(atoms)[index]
+                coordinates = coordinates[index]
+        dd[self.atoms] = atoms
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class CroppingPocketDataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, coordinates, max_atoms=256):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.max_atoms = (
+            max_atoms  # max number of atoms in a molecule, None indicates no limit.
+        )
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        atoms = dd[self.atoms]
+        coordinates = dd[self.coordinates]
+        #residue = dd["residue"]
+
+        # crop atoms according to their distance to the center of pockets
+        if self.max_atoms and len(atoms) > self.max_atoms:
+            with data_utils.numpy_seed(self.seed, epoch, index):
+                distance = np.linalg.norm(
+                    coordinates - coordinates.mean(axis=0), axis=1
+                )
+
+                def softmax(x):
+                    x -= np.max(x)
+                    x = np.exp(x) / np.sum(np.exp(x))
+                    return x
+
+                distance += 1  # prevent inf
+                weight = softmax(np.reciprocal(distance))
+                index = np.random.choice(
+                    len(atoms), self.max_atoms, replace=False, p=weight
+                )
+                atoms = atoms[index]
+                coordinates = coordinates[index]
+                #residue = residue[index]
+
+        dd[self.atoms] = atoms
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        #dd["residue"] = residue
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class CroppingResiduePocketDataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, residues, coordinates, max_atoms=256):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.residues = residues
+        self.coordinates = coordinates
+        self.max_atoms = (
+            max_atoms  # max number of atoms in a molecule, None indicates no limit.
+        )
+
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        atoms = dd[self.atoms]
+        residues = dd[self.residues]
+        coordinates = dd[self.coordinates]
+
+        residues_distance_map = {}
+
+        # crop atoms according to their distance to the center of pockets
+        if self.max_atoms and len(atoms) > self.max_atoms:
+            with data_utils.numpy_seed(self.seed, epoch, index):
+                distance = np.linalg.norm(
+                    coordinates - coordinates.mean(axis=0), axis=1
+                )
+                residues_ids, residues_distance = [], []
+                for res in residues:
+                    if res not in residues_ids:
+                        residues_ids.append(res)
+                        residues_distance.append(distance[residues == res].mean())
+                residues_ids = np.array(residues_ids)
+                residues_distance = np.array(residues_distance)
+
+                def softmax(x):
+                    x -= np.max(x)
+                    x = np.exp(x) / np.sum(np.exp(x))
+                    return x
+
+                residues_distance += 1  # prevent inf and smoothing out the distance
+                weight = softmax(np.reciprocal(residues_distance))
+                max_residues = self.max_atoms // (len(atoms) // (len(residues_ids) + 1))
+                if max_residues < 1:
+                    max_residues += 1
+                max_residues = min(max_residues, len(residues_ids))
+                residue_index = np.random.choice(
+                    len(residues_ids), max_residues, replace=False, p=weight
+                )
+                index = [
+                    i
+                    for i in range(len(atoms))
+                    if residues[i] in residues_ids[residue_index]
+                ]
+                atoms = atoms[index]
+                coordinates = coordinates[index]
+                residues = residues[index]
+
+        dd[self.atoms] = atoms
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        dd[self.residues] = residues
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class CroppingPocketDockingPoseDataset(BaseWrapperDataset):
+    def __init__(
+        self, dataset, seed, atoms, coordinates, holo_coordinates, max_atoms=256
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates 
+        self.max_atoms = max_atoms
+
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        atoms = dd[self.atoms]
+        coordinates = dd[self.coordinates]
+        holo_coordinates = dd[self.holo_coordinates]
+
+        # crop atoms according to their distance to the center of pockets
+        #print(len(atoms))
+        if self.max_atoms and len(atoms) > self.max_atoms:
+            with data_utils.numpy_seed(self.seed, 1):
+                distance = np.linalg.norm(
+                    coordinates - coordinates.mean(axis=0), axis=1
+                )
+
+                def softmax(x):
+                    x -= np.max(x)
+                    x = np.exp(x) / np.sum(np.exp(x))
+                    return x
+
+                distance += 1  # prevent inf
+                weight = softmax(np.reciprocal(distance))
+                index = np.random.choice(
+                    len(atoms), self.max_atoms, replace=False, p=weight
+                )
+                atoms = atoms[index]
+                coordinates = coordinates[index]
+                holo_coordinates = holo_coordinates[index]
+
+        dd[self.atoms] = atoms
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        dd[self.holo_coordinates] = holo_coordinates.astype(np.float32)
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+class CroppingPocketDockingPoseTestDataset(BaseWrapperDataset):
+    def __init__(
+        self, dataset, seed, atoms, coordinates, max_atoms=256
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.max_atoms = max_atoms
+
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        atoms = dd[self.atoms]
+        coordinates = dd[self.coordinates]
+
+        # crop atoms according to their distance to the center of pockets
+        if self.max_atoms and len(atoms) > self.max_atoms:
+            with data_utils.numpy_seed(1, 1):
+                distance = np.linalg.norm(
+                    coordinates - coordinates.mean(axis=0), axis=1
+                )
+
+                def softmax(x):
+                    x -= np.max(x)
+                    x = np.exp(x) / np.sum(np.exp(x))
+                    return x
+
+                distance += 1  # prevent inf
+                weight = softmax(np.reciprocal(distance))
+                index = np.random.choice(
+                    len(atoms), self.max_atoms, replace=False, p=weight
+                )
+                atoms = atoms[index]
+                coordinates = coordinates[index]
+
+        dd[self.atoms] = atoms
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)

data/unimol/data/data_utils.py

@@ -0,0 +1,23 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import contextlib
+
+
+@contextlib.contextmanager
+def numpy_seed(seed, *addl_seeds):
+    """Context manager which seeds the NumPy PRNG with the specified seed and
+    restores the state afterward"""
+    if seed is None:
+        yield
+        return
+    if len(addl_seeds) > 0:
+        seed = int(hash((seed, *addl_seeds)) % 1e6)
+    state = np.random.get_state()
+    np.random.seed(seed)
+    try:
+        yield
+    finally:
+        np.random.set_state(state)

data/unimol/data/dictionary.py

@@ -0,0 +1,157 @@
+# Copyright (c) DP Technology.
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+import logging
+
+import numpy as np
+
+logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
+class DecoderDictionary:
+    """A mapping from symbols to consecutive integers"""
+
+    def __init__(
+        self,
+        *,  # begin keyword-only arguments
+        bos="[CLS]",
+        pad="[PAD]",
+        eos="[SEP]",
+        unk="[UNK]",
+        extra_special_symbols=None,
+    ):
+        self.bos_word, self.unk_word, self.pad_word, self.eos_word = bos, unk, pad, eos
+        self.symbols = []
+        self.count = []
+        self.indices = {}
+        self.idx2sym = {}
+        self.specials = set()
+        self.specials.add(bos)
+        self.specials.add(unk)
+        self.specials.add(pad)
+        self.specials.add(eos)
+
+    def __eq__(self, other):
+        return self.indices == other.indices
+
+    def __getitem__(self, idx):
+        if idx < len(self.symbols):
+            return self.symbols[idx]
+        return self.unk_word
+
+    def __len__(self):
+        """Returns the number of symbols in the dictionary"""
+        return len(self.symbols)
+
+    def __contains__(self, sym):
+        return sym in self.indices
+
+    def vec_index(self, a):
+        return np.vectorize(self.index)(a)
+
+    def index(self, sym):
+        """Returns the index of the specified symbol"""
+        assert isinstance(sym, str)
+        if sym in self.indices:
+            return self.indices[sym]
+        return self.indices[self.unk_word]
+    
+    def index2symbol(self, idx):
+        """Returns the corresponding symbol of the specified index"""
+        assert isinstance(idx, int)
+        if idx in self.idx2sym:
+            return self.idx2sym[idx]
+        return self.unk_word
+    
+    def special_index(self):
+        return [self.index(x) for x in self.specials]
+
+    def add_symbol(self, word, n=1, overwrite=False, is_special=False):
+        """Adds a word to the dictionary"""
+        if is_special:
+            self.specials.add(word)
+        if word in self.indices and not overwrite:
+            idx = self.indices[word]
+            self.count[idx] = self.count[idx] + n
+            return idx
+        else:
+            idx = len(self.symbols)
+            self.indices[word] = idx
+            self.idx2sym[idx] = word
+            self.symbols.append(word)
+            self.count.append(n)
+            return idx
+
+    def bos(self):
+        """Helper to get index of beginning-of-sentence symbol"""
+        return self.index(self.bos_word)
+
+    def pad(self):
+        """Helper to get index of pad symbol"""
+        return self.index(self.pad_word)
+
+    def eos(self):
+        """Helper to get index of end-of-sentence symbol"""
+        return self.index(self.eos_word)
+
+    def unk(self):
+        """Helper to get index of unk symbol"""
+        return self.index(self.unk_word)
+
+    @classmethod
+    def load(cls, f):
+        """Loads the dictionary from a text file with the format:
+
+        ```
+        <symbol0> <count0>
+        <symbol1> <count1>
+        ...
+        ```
+        """
+        d = cls()
+        d.add_from_file(f)
+        return d
+
+    def add_from_file(self, f):
+        """
+        Loads a pre-existing dictionary from a text file and adds its symbols
+        to this instance.
+        """
+        if isinstance(f, str):
+            try:
+                with open(f, "r", encoding="utf-8") as fd:
+                    self.add_from_file(fd)
+            except FileNotFoundError as fnfe:
+                raise fnfe
+            except UnicodeError:
+                raise Exception(
+                    "Incorrect encoding detected in {}, please "
+                    "rebuild the dataset".format(f)
+                )
+            return
+
+        lines = f.readlines()
+
+        for line_idx, line in enumerate(lines):
+            try:
+                splits = line.rstrip().rsplit(" ", 1)
+                line = splits[0]
+                field = splits[1] if len(splits) > 1 else str(len(lines) - line_idx)
+                if field == "#overwrite":
+                    overwrite = True
+                    line, field = line.rsplit(" ", 1)
+                else:
+                    overwrite = False
+                count = int(field)
+                word = line
+                if word in self and not overwrite:
+                    logger.info(
+                        "Duplicate word found when loading Dictionary: '{}', index is {}.".format(word, self.indices[word])
+                    )
+                else:
+                    self.add_symbol(word, n=count, overwrite=overwrite)
+            except ValueError:
+                raise ValueError(
+                    "Incorrect dictionary format, expected '<token> <cnt> [flags]'"
+                )

data/unimol/data/distance_dataset.py

@@ -0,0 +1,64 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+import torch
+from scipy.spatial import distance_matrix
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+
+
+class DistanceDataset(BaseWrapperDataset):
+    def __init__(self, dataset):
+        super().__init__(dataset)
+        self.dataset = dataset
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, idx):
+        pos = self.dataset[idx].view(-1, 3).numpy()
+        dist = distance_matrix(pos, pos).astype(np.float32)
+        return torch.from_numpy(dist)
+
+
+class EdgeTypeDataset(BaseWrapperDataset):
+    def __init__(self, dataset: torch.utils.data.Dataset, num_types: int):
+        self.dataset = dataset
+        self.num_types = num_types
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, index: int):
+        node_input = self.dataset[index].clone()
+        offset = node_input.view(-1, 1) * self.num_types + node_input.view(1, -1)
+        return offset
+
+
+class CrossDistanceDataset(BaseWrapperDataset):
+    def __init__(self, mol_dataset, pocket_dataset):
+        super().__init__(mol_dataset)
+        self.dataset = mol_dataset
+        self.mol_dataset = mol_dataset
+        self.pocket_dataset = pocket_dataset
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, idx):
+        mol_pos = self.mol_dataset[idx].view(-1, 3).numpy()
+        pocket_pos = self.pocket_dataset[idx].view(-1, 3).numpy()
+        dist = distance_matrix(mol_pos, pocket_pos).astype(np.float32)
+        assert dist.shape[0] == self.mol_dataset[idx].shape[0]
+        assert dist.shape[1] == self.pocket_dataset[idx].shape[0]
+        return torch.from_numpy(dist)
+
+class CrossEdgeTypeDataset(BaseWrapperDataset):
+    def __init__(self, mol_dataset, pocket_dataset, num_types: int):
+        self.dataset = mol_dataset
+        self.mol_dataset = mol_dataset
+        self.pocket_dataset = pocket_dataset
+        self.num_types = num_types
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, index: int):
+        mol_node_input = self.mol_dataset[index].clone()
+        pocket_node_input = self.pocket_dataset[index].clone()
+        offset = mol_node_input.view(-1, 1) * self.num_types + pocket_node_input.view(1, -1)
+        return offset

data/unimol/data/from_str_dataset.py

@@ -0,0 +1,19 @@
+import torch
+from functools import lru_cache
+from unicore.data import UnicoreDataset
+
+
+class FromStrLabelDataset(UnicoreDataset):
+    def __init__(self, labels):
+        super().__init__()
+        self.labels = labels
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, index):
+        return self.labels[index]
+
+    def __len__(self):
+        return len(self.labels)
+
+    def collater(self, samples):
+        return torch.tensor(list(map(float, samples)))

data/unimol/data/key_dataset.py

@@ -0,0 +1,29 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+
+
+class KeyDataset(BaseWrapperDataset):
+    def __init__(self, dataset, key):
+        self.dataset = dataset
+        self.key = key
+
+    def __len__(self):
+        return len(self.dataset)
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, idx):
+        return self.dataset[idx][self.key]
+
+class LengthDataset(BaseWrapperDataset):
+
+    def __init__(self, dataset):
+        super().__init__(dataset)
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, idx):
+        item = self.dataset[idx]
+        return len(item)

data/unimol/data/lmdb_dataset.py

@@ -0,0 +1,49 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import lmdb
+import os
+import pickle
+from functools import lru_cache
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class LMDBDataset:
+    def __init__(self, db_path):
+        self.db_path = db_path
+        assert os.path.isfile(self.db_path), "{} not found".format(self.db_path)
+        env = self.connect_db(self.db_path)
+        with env.begin() as txn:
+            self._keys = list(txn.cursor().iternext(values=False))
+
+    def connect_db(self, lmdb_path, save_to_self=False):
+        env = lmdb.open(
+            lmdb_path,
+            subdir=False,
+            readonly=True,
+            lock=False,
+            readahead=False,
+            meminit=False,
+            max_readers=256,
+        )
+        if not save_to_self:
+            return env
+        else:
+            self.env = env
+
+    def __len__(self):
+        return len(self._keys)
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, idx):
+        if not hasattr(self, "env"):
+            self.connect_db(self.db_path, save_to_self=True)
+        #datapoint_pickled = self.env.begin().get(f"{idx}".encode("ascii"))
+        #print(idx)
+        datapoint_pickled = self.env.begin().get(f"{idx}".encode("ascii"))
+        data = pickle.loads(datapoint_pickled)
+        return data

data/unimol/data/mask_points_dataset.py

@@ -0,0 +1,267 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import lru_cache
+
+import numpy as np
+import torch
+from unicore.data import Dictionary
+from unicore.data import BaseWrapperDataset
+from . import data_utils
+
+
+class MaskPointsDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset: torch.utils.data.Dataset,
+        coord_dataset: torch.utils.data.Dataset,
+        vocab: Dictionary,
+        pad_idx: int,
+        mask_idx: int,
+        noise_type: str,
+        noise: float = 1.0,
+        seed: int = 1,
+        mask_prob: float = 0.15,
+        leave_unmasked_prob: float = 0.1,
+        random_token_prob: float = 0.1,
+    ):
+        assert 0.0 < mask_prob < 1.0
+        assert 0.0 <= random_token_prob <= 1.0
+        assert 0.0 <= leave_unmasked_prob <= 1.0
+        assert random_token_prob + leave_unmasked_prob <= 1.0
+
+        self.dataset = dataset
+        self.coord_dataset = coord_dataset
+        self.vocab = vocab
+        self.pad_idx = pad_idx
+        self.mask_idx = mask_idx
+        self.noise_type = noise_type
+        self.noise = noise
+        self.seed = seed
+        self.mask_prob = mask_prob
+        self.leave_unmasked_prob = leave_unmasked_prob
+        self.random_token_prob = random_token_prob
+
+        if random_token_prob > 0.0:
+            weights = np.ones(len(self.vocab))
+            weights[vocab.special_index()] = 0
+            self.weights = weights / weights.sum()
+
+        self.epoch = None
+        if self.noise_type == "trunc_normal":
+            self.noise_f = lambda num_mask: np.clip(
+                np.random.randn(num_mask, 3) * self.noise,
+                a_min=-self.noise * 2.0,
+                a_max=self.noise * 2.0,
+            )
+        elif self.noise_type == "normal":
+            self.noise_f = lambda num_mask: np.random.randn(num_mask, 3) * self.noise
+        elif self.noise_type == "uniform":
+            self.noise_f = lambda num_mask: np.random.uniform(
+                low=-self.noise, high=self.noise, size=(num_mask, 3)
+            )
+        else:
+            self.noise_f = lambda num_mask: 0.0
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.coord_dataset.set_epoch(epoch)
+        self.dataset.set_epoch(epoch)
+        self.epoch = epoch
+
+    def __getitem__(self, index: int):
+        return self.__getitem_cached__(self.epoch, index)
+
+    @lru_cache(maxsize=16)
+    def __getitem_cached__(self, epoch: int, index: int):
+        ret = {}
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            item = self.dataset[index]
+            coord = self.coord_dataset[index]
+            sz = len(item)
+            # don't allow empty sequence
+            assert sz > 0
+            # decide elements to mask
+            num_mask = int(
+                # add a random number for probabilistic rounding
+                self.mask_prob * sz
+                + np.random.rand()
+            )
+            mask_idc = np.random.choice(sz, num_mask, replace=False)
+            mask = np.full(sz, False)
+            mask[mask_idc] = True
+            ret["targets"] = np.full(len(mask), self.pad_idx)
+            ret["targets"][mask] = item[mask]
+            ret["targets"] = torch.from_numpy(ret["targets"]).long()
+            # decide unmasking and random replacement
+            rand_or_unmask_prob = self.random_token_prob + self.leave_unmasked_prob
+            if rand_or_unmask_prob > 0.0:
+                rand_or_unmask = mask & (np.random.rand(sz) < rand_or_unmask_prob)
+                if self.random_token_prob == 0.0:
+                    unmask = rand_or_unmask
+                    rand_mask = None
+                elif self.leave_unmasked_prob == 0.0:
+                    unmask = None
+                    rand_mask = rand_or_unmask
+                else:
+                    unmask_prob = self.leave_unmasked_prob / rand_or_unmask_prob
+                    decision = np.random.rand(sz) < unmask_prob
+                    unmask = rand_or_unmask & decision
+                    rand_mask = rand_or_unmask & (~decision)
+            else:
+                unmask = rand_mask = None
+
+            if unmask is not None:
+                mask = mask ^ unmask
+
+            new_item = np.copy(item)
+            new_item[mask] = self.mask_idx
+
+            num_mask = mask.astype(np.int32).sum()
+            new_coord = np.copy(coord)
+            new_coord[mask, :] += self.noise_f(num_mask)
+
+            if rand_mask is not None:
+                num_rand = rand_mask.sum()
+                if num_rand > 0:
+                    new_item[rand_mask] = np.random.choice(
+                        len(self.vocab),
+                        num_rand,
+                        p=self.weights,
+                    )
+            ret["atoms"] = torch.from_numpy(new_item).long()
+            ret["coordinates"] = torch.from_numpy(new_coord).float()
+            return ret
+
+
+class MaskPointsPocketDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset: torch.utils.data.Dataset,
+        coord_dataset: torch.utils.data.Dataset,
+        residue_dataset: torch.utils.data.Dataset,
+        vocab: Dictionary,
+        pad_idx: int,
+        mask_idx: int,
+        noise_type: str,
+        noise: float = 1.0,
+        seed: int = 1,
+        mask_prob: float = 0.15,
+        leave_unmasked_prob: float = 0.1,
+        random_token_prob: float = 0.1,
+    ):
+        assert 0.0 < mask_prob < 1.0
+        assert 0.0 <= random_token_prob <= 1.0
+        assert 0.0 <= leave_unmasked_prob <= 1.0
+        assert random_token_prob + leave_unmasked_prob <= 1.0
+
+        self.dataset = dataset
+        self.coord_dataset = coord_dataset
+        self.residue_dataset = residue_dataset
+        self.vocab = vocab
+        self.pad_idx = pad_idx
+        self.mask_idx = mask_idx
+        self.noise_type = noise_type
+        self.noise = noise
+        self.seed = seed
+        self.mask_prob = mask_prob
+        self.leave_unmasked_prob = leave_unmasked_prob
+        self.random_token_prob = random_token_prob
+
+        if random_token_prob > 0.0:
+            weights = np.ones(len(self.vocab))
+            weights[vocab.special_index()] = 0
+            self.weights = weights / weights.sum()
+
+        self.epoch = None
+        if self.noise_type == "trunc_normal":
+            self.noise_f = lambda num_mask: np.clip(
+                np.random.randn(num_mask, 3) * self.noise,
+                a_min=-self.noise * 2.0,
+                a_max=self.noise * 2.0,
+            )
+        elif self.noise_type == "normal":
+            self.noise_f = lambda num_mask: np.random.randn(num_mask, 3) * self.noise
+        elif self.noise_type == "uniform":
+            self.noise_f = lambda num_mask: np.random.uniform(
+                low=-self.noise, high=self.noise, size=(num_mask, 3)
+            )
+        else:
+            self.noise_f = lambda num_mask: 0.0
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.coord_dataset.set_epoch(epoch)
+        self.dataset.set_epoch(epoch)
+        self.epoch = epoch
+
+    def __getitem__(self, index: int):
+        return self.__getitem_cached__(self.epoch, index)
+
+    @lru_cache(maxsize=16)
+    def __getitem_cached__(self, epoch: int, index: int):
+        ret = {}
+        with data_utils.numpy_seed(self.seed, epoch, index):
+            item = self.dataset[index]
+            coord = self.coord_dataset[index]
+            sz = len(item)
+            # don't allow empty sequence
+            assert sz > 0
+
+            # mask on the level of residues
+            residue = self.residue_dataset[index]
+            res_list = list(set(residue))
+            res_sz = len(res_list)
+
+            # decide elements to mask
+            num_mask = int(
+                # add a random number for probabilistic rounding
+                self.mask_prob * res_sz
+                + np.random.rand()
+            )
+            mask_res = np.random.choice(res_list, num_mask, replace=False).tolist()
+            mask = np.isin(residue, mask_res)
+
+            ret["targets"] = np.full(len(mask), self.pad_idx)
+            ret["targets"][mask] = item[mask]
+            ret["targets"] = torch.from_numpy(ret["targets"]).long()
+            # decide unmasking and random replacement
+            rand_or_unmask_prob = self.random_token_prob + self.leave_unmasked_prob
+            if rand_or_unmask_prob > 0.0:
+                rand_or_unmask = mask & (np.random.rand(sz) < rand_or_unmask_prob)
+                if self.random_token_prob == 0.0:
+                    unmask = rand_or_unmask
+                    rand_mask = None
+                elif self.leave_unmasked_prob == 0.0:
+                    unmask = None
+                    rand_mask = rand_or_unmask
+                else:
+                    unmask_prob = self.leave_unmasked_prob / rand_or_unmask_prob
+                    decision = np.random.rand(sz) < unmask_prob
+                    unmask = rand_or_unmask & decision
+                    rand_mask = rand_or_unmask & (~decision)
+            else:
+                unmask = rand_mask = None
+
+            if unmask is not None:
+                mask = mask ^ unmask
+
+            new_item = np.copy(item)
+            new_item[mask] = self.mask_idx
+
+            num_mask = mask.astype(np.int32).sum()
+            new_coord = np.copy(coord)
+            new_coord[mask, :] += self.noise_f(num_mask)
+
+            if rand_mask is not None:
+                num_rand = rand_mask.sum()
+                if num_rand > 0:
+                    new_item[rand_mask] = np.random.choice(
+                        len(self.vocab),
+                        num_rand,
+                        p=self.weights,
+                    )
+            ret["atoms"] = torch.from_numpy(new_item).long()
+            ret["coordinates"] = torch.from_numpy(new_coord).float()
+            return ret

data/unimol/data/normalize_dataset.py

@@ -0,0 +1,68 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+
+
+class NormalizeDataset(BaseWrapperDataset):
+    def __init__(self, dataset, coordinates, normalize_coord=True):
+        self.dataset = dataset
+        self.coordinates = coordinates
+        self.normalize_coord = normalize_coord  # normalize the coordinates.
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        coordinates = dd[self.coordinates]
+        # normalize
+        if self.normalize_coord:
+            coordinates = coordinates - coordinates.mean(axis=0)
+            dd[self.coordinates] = coordinates.astype(np.float32)
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class NormalizeDockingPoseDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        coordinates,
+        pocket_coordinates,
+        center_coordinates="center_coordinates",
+    ):
+        self.dataset = dataset
+        self.coordinates = coordinates
+        self.pocket_coordinates = pocket_coordinates
+        self.center_coordinates = center_coordinates
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        coordinates = dd[self.coordinates]
+        pocket_coordinates = dd[self.pocket_coordinates]
+        # normalize coordinates and pocket coordinates ,align with pocket center coordinates
+        center_coordinates = pocket_coordinates.mean(axis=0)
+        coordinates = coordinates - center_coordinates
+        pocket_coordinates = pocket_coordinates - center_coordinates
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        dd[self.pocket_coordinates] = pocket_coordinates.astype(np.float32)
+        dd[self.center_coordinates] = center_coordinates.astype(np.float32)
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)

data/unimol/data/plasma_utils.py

@@ -0,0 +1,188 @@
+import hashlib
+import json
+import subprocess
+import tempfile
+from typing import Hashable
+
+try:
+    import pyarrow.plasma as plasma
+
+    PYARROW_AVAILABLE = True
+except ImportError:
+    plasma = None
+    PYARROW_AVAILABLE = False
+
+
+class PlasmaArray:
+    """
+    Wrapper around numpy arrays that automatically moves the data to shared
+    memory upon serialization. This is particularly helpful when passing numpy
+    arrays through multiprocessing, so that data is not unnecessarily
+    duplicated or pickled.
+    """
+
+    def __init__(self, array):
+        super().__init__()
+        self.array = array
+        self.disable = array.nbytes < 134217728  # disable for arrays <128MB
+        self.object_id = None
+        self.path = None
+
+        # variables with underscores shouldn't be pickled
+        self._client = None
+        self._server = None
+        self._server_tmp = None
+        self._plasma = None
+
+    @property
+    def plasma(self):
+        if self._plasma is None and not self.disable:
+            self._plasma = plasma
+        return self._plasma
+
+    def start_server(self):
+        if self.plasma is None or self._server is not None:
+            return
+        assert self.object_id is None
+        assert self.path is None
+        self._server_tmp = tempfile.NamedTemporaryFile()
+        self.path = self._server_tmp.name
+        self._server = subprocess.Popen(
+            ["plasma_store", "-m", str(int(1.05 * self.array.nbytes)), "-s", self.path]
+        )
+
+    @property
+    def client(self):
+        if self._client is None:
+            assert self.path is not None
+            self._client = self.plasma.connect(self.path, num_retries=200)
+        return self._client
+
+    def __getstate__(self):
+        """Called on pickle load"""
+        if self.plasma is None:
+            return self.__dict__
+        if self.object_id is None:
+            self.start_server()
+            self.object_id = self.client.put(self.array)
+        state = self.__dict__.copy()
+        del state["array"]
+        state["_client"] = None
+        state["_server"] = None
+        state["_server_tmp"] = None
+        state["_plasma"] = None
+        return state
+
+    def __setstate__(self, state):
+        """Called on pickle save"""
+        self.__dict__.update(state)
+        if self.plasma is None:
+            return
+        self.array = self.client.get(self.object_id)
+
+    def __del__(self):
+        if self._server is not None:
+            self._server.kill()
+            self._server = None
+            self._server_tmp.close()
+            self._server_tmp = None
+
+
+DEFAULT_PLASMA_PATH = "/tmp/plasma"
+
+
+class PlasmaView:
+    """Interface to write and read from shared memory. Whereas PlasmaArray writes to plasma on serialization,
+    PlasmaView writes to shared memory on instantiation."""
+
+    def __init__(self, array, split_path: str, hash_data: Hashable, plasma_path=None):
+        """
+        Args:
+            array: numpy array to store. This can be read with ``PlasmaView().array``
+            split_path: the path whence the data was read, used for hashing
+            hash_data: other metadata about the array that can be used to create a unique key.
+                as of writing, the 3 callers in ``TokenBlockDataset`` use::
+                    hash_data = ((block_size, document_sep_len, str(break_mode), len(dataset)), 0|1|2)
+        """
+        assert PYARROW_AVAILABLE
+        assert split_path is not None
+        if plasma_path is None:
+            plasma_path = DEFAULT_PLASMA_PATH
+
+        self.path = plasma_path
+        self.split_path = split_path
+        self._client = None  # Initialize lazily for pickle. plasma clients should not be deep copied or serialized.
+        self._n = None
+
+        self.object_id = self.get_object_id(self.split_path, hash_data)
+        try:
+            self.client.put(array, object_id=self.object_id)
+        except plasma.PlasmaObjectExists:
+            pass
+
+    @property
+    def client(self):
+        if self._client is None:
+            self._client = plasma.connect(self.path, num_retries=200)
+        return self._client
+
+    @property
+    def array(self):
+        """Fetch a read only view of an np.array, stored in plasma."""
+        ret = self.client.get(self.object_id)
+        return ret
+
+    @staticmethod
+    def get_object_id(split_path: str, hash_data: Hashable):
+        """Returns plasma.ObjectID from hashing split_path and object_num."""
+        hash = hashlib.blake2b(bytes(split_path, "utf-8"), digest_size=20)
+        harg = json.dumps(hash_data).encode("utf-8")
+        hash.update(harg)
+        return plasma.ObjectID(hash.digest())
+
+    def __getstate__(self):
+        """Called on pickle save"""
+        self.disconnect()
+        state = self.__dict__.copy()
+        assert state["_client"] is None
+        assert "object_id" in state
+        return state
+
+    def __setstate__(self, state):
+        """Called on pickle load"""
+        self.__dict__.update(state)
+
+    def __del__(self):
+        self.disconnect()
+
+    def disconnect(self):
+        if self._client is not None:
+            self._client.disconnect()
+            self._client = None
+
+    def __len__(self):
+        """Save reads by caching len"""
+        if self._n is None:
+            self._n = len(self.array)
+        return self._n
+
+
+GB100 = (1024**3) * 100
+
+
+class PlasmaStore:
+    def __init__(self, path=DEFAULT_PLASMA_PATH, nbytes: int = GB100):
+
+        self.server = self.start(path, nbytes)
+
+    def __del__(self):
+        self.server.kill()
+
+    @staticmethod
+    def start(path=DEFAULT_PLASMA_PATH, nbytes: int = GB100) -> subprocess.Popen:
+        if not PYARROW_AVAILABLE:
+            raise ImportError("please run pip install pyarrow to use --use_plasma_view")
+        # best practice is to allocate more space than we need. The limitation seems to be the size of /dev/shm
+        _server = subprocess.Popen(["plasma_store", "-m", str(nbytes), "-s", path])
+        plasma.connect(path, num_retries=200)  # If we can't connect we fail immediately
+        return _server

data/unimol/data/pocket2mol_dataset.py

@@ -0,0 +1,403 @@
+from functools import lru_cache
+import sys
+import pickle
+import random
+import networkx as nx
+import numpy as np
+import torch
+import rdkit
+from rdkit import Chem
+from rdkit.Chem import AllChem
+sys.path.append('..')
+
+import numpy as np
+from unicore.data import BaseWrapperDataset
+
+from . import data_utils
+from unimol.utils import geom
+
+def gen_conformation(mol, num_conf=20, num_worker=8, keepHs=False):
+    try:
+        mol = Chem.AddHs(mol)
+        AllChem.EmbedMultipleConfs(mol, numConfs=num_conf, numThreads=num_worker, pruneRmsThresh=0.1, maxAttempts=5, useRandomCoords=False)
+        AllChem.MMFFOptimizeMoleculeConfs(mol, numThreads=num_worker)
+        if not keepHs:
+            mol = Chem.RemoveHs(mol)
+        return mol
+    except:
+        return None
+
+class FragmentConformationDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        vocab,
+        conf_vocab,
+        use_pocket=True,
+        is_train=True
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.use_pocket = use_pocket
+        self.conf_vocab = Vocabulary(vocab, conf_vocab)
+        self.is_train = is_train
+        self.set_epoch(None)
+    
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+        
+
+    def parse_frag_mol(self, frag_mol):
+        atom_types = [a.GetSymbol() for a in frag_mol.GetAtoms()]
+        atom_coords = np.array(frag_mol.GetConformer(0).GetPositions())
+        return {'atom_types': atom_types, 'atom_coords': atom_coords}
+    
+    def parse_frag_idx(self, vocab_conf, full_mol, atom_map):
+        if vocab_conf.GetNumConformers() == 0:
+            smi = Chem.MolToSmiles(vocab_conf)
+            vocab_conf = Chem.MolFromSmiles(smi)
+            vocab_conf = gen_conformation(vocab_conf, num_conf=1, num_worker=1, keepHs=True)
+        mol = Chem.RWMol(full_mol)
+        atom_idx = list(range(full_mol.GetNumAtoms()))
+        for i, atom in enumerate(full_mol.GetAtoms()):
+            if atom.GetAtomMapNum() not in atom_map:
+                atom_idx[i] = -1
+        for i in range(len(atom_idx) - 1, -1, -1):
+            if atom_idx[i] == -1:
+                mol.RemoveAtom(i)
+        mol = mol.GetMol()
+        #mol = Chem.RemoveHs(mol)
+        smi = Chem.MolToSmiles(mol)
+        #find the map num in smiles
+        map_num = []
+        smi_p = smi.split('[')
+        for i in range(1, len(smi_p)):
+            if ':' in smi_p[i]:
+                end_idx = smi_p[i].split(':')[1].index(']')
+                map_num.append(int(smi_p[i].split(':')[1][:end_idx]))
+
+        vocab_conf = Chem.RemoveHs(vocab_conf)
+        for i, atom in enumerate(vocab_conf.GetAtoms()):
+            if atom.GetSymbol() != 'H':
+                atom.SetAtomMapNum(map_num[i])
+        vocab_conf = Chem.AddHs(vocab_conf, addCoords=True)
+
+        if torch.isnan(torch.from_numpy(np.array(vocab_conf.GetConformer(0).GetPositions()))).any():
+            vocab_conf = gen_conformation(vocab_conf, num_conf=1, num_worker=1, keepHs=True)
+
+        return vocab_conf
+
+
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+    
+    def check_leaf(self, edges, index):
+        out_degree = 0
+        for edge in edges:
+            if edge[0] == index:
+                out_degree += 1
+        if out_degree == 0:
+            return True
+        else:
+            return False
+
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+    
+    def check_leaf(self, edges, index):
+        out_degree = 0
+        for edge in edges:
+            if edge[0] == index:
+                out_degree += 1
+        if out_degree == 0:
+            return True
+        else:
+            return False
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        
+        random.seed(self.seed + epoch)
+        #pocket
+        if self.use_pocket:
+            pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index]['pocket_atom']]
+        )
+        pocket_coordinates = np.stack(self.dataset[index]['pocket_coord'])
+
+        full_mol = self.dataset[index]['frags']['mol']
+
+        frag_graph = nx.Graph()
+        edges = self.dataset[index]['frags']['frag_edges']
+        frag_graph.add_edges_from(np.array(edges))
+        if len(edges) == 0:
+            frag_mol_idx = self.dataset[index]['frags']['frag_idx'][0]
+            frag_mol = self.conf_vocab.conf[frag_mol_idx]
+            f_mol_noH_num = len([atom for atom in frag_mol.GetAtoms() if atom.GetSymbol() != 'H'])
+            frag_mol = Chem.AddHs(frag_mol, addCoords=True)
+            frag_mol_data = self.parse_frag_mol(frag_mol)
+            return {
+                'atom_types': np.array([], dtype=str),
+                'atom_coords': np.zeros((1, 3), dtype=np.float32),
+                'focal_atom_local': 0,#place holder
+                'attach_atom_local': 0,#place holder
+                'focal_atom': 0,#place holder
+                'attach_atom': 0,#place holder
+                'frag_atom_types': frag_mol_data['atom_types'],
+                'frag_atom_coords': frag_mol_data['atom_coords'],
+                'end': True,
+                'torsion_output_prev': 0,#place holder
+                'coords_input_prev': frag_mol_data['atom_coords'],
+                'atom_types_withfocal': frag_mol_data['atom_types'],
+                'pocket_atoms': pocket_atoms,
+                'pocket_coordinates': pocket_coordinates,
+                'first': True,
+                'symmetric': True
+            }
+        bfs_edges = list(nx.bfs_edges(frag_graph, 0))
+        #reorder the link and local link
+        link = self.dataset[index]['frags']['links']
+        local_link = self.dataset[index]['frags']['links_local']
+        reorrder_link, reorrder_local_link = [], []
+        for i, b_edge in enumerate(bfs_edges):
+            for j, o_edge in enumerate(edges):
+                if b_edge[0] == o_edge[0] and b_edge[1] == o_edge[1]:
+                    reorrder_link.append(link[j])
+                    reorrder_local_link.append(local_link[j])
+                elif b_edge[0] == o_edge[1] and b_edge[1] == o_edge[0]:
+                    reorrder_link.append([link[j][1], link[j][0]])
+                    reorrder_local_link.append([local_link[j][1], local_link[j][0]])
+        self.dataset[index]['frags']['links'] = reorrder_link
+        self.dataset[index]['frags']['links_local'] = reorrder_local_link
+        #clip a random subgraph
+        bfs_edges_full = bfs_edges.copy()
+        clip_step = random.randint(0, len(bfs_edges))
+        #print('clip_step', clip_step)
+        start_frag = bfs_edges[0][0]
+        if clip_step != 0:
+            bfs_edges = bfs_edges[:clip_step]
+            focal_frag, attach_frag = bfs_edges[-1]
+        else:
+            focal_frag, attach_frag = bfs_edges[0]
+
+        for i, atom in enumerate(full_mol.GetAtoms()):
+            atom.SetAtomMapNum(i + 1)
+        
+        end = (clip_step == len(bfs_edges_full))
+        if clip_step == 0:
+            #frag
+            #print('first frag')
+            frag_mol_idx = self.dataset[index]['frags']['frag_idx'][start_frag]
+            frag_mol = self.conf_vocab.conf[frag_mol_idx]
+            if frag_mol is None:
+                print(frag_mol_idx, 'is None')
+            frag_mol = self.parse_frag_idx(frag_mol, full_mol, self.dataset[index]['frags']['map'][start_frag])
+            frag_mol_data = self.parse_frag_mol(frag_mol)
+            return {
+                'atom_types': np.array([], dtype=str),
+                'atom_coords': np.zeros((1, 3), dtype=np.float32),
+                'focal_atom_local': 0,#place holder
+                'attach_atom_local': 0,#place holder
+                'focal_atom': 0,#place holder
+                'attach_atom': 0,#place holder
+                'frag_atom_types': frag_mol_data['atom_types'],
+                'frag_atom_coords': frag_mol_data['atom_coords'],
+                'end': end,
+                'torsion_output_prev': 0,#place holder
+                'coords_input_prev': frag_mol_data['atom_coords'],
+                'atom_types_withfocal': frag_mol_data['atom_types'],
+                'pocket_atoms': pocket_atoms,
+                'pocket_coordinates': pocket_coordinates,
+                'first': True,
+                'symmetric': True
+            }
+        
+        
+        clip_frag_idx = [e[0] for e in bfs_edges] + [e[1] for e in bfs_edges[:-1]]
+        clip_frag_idx = np.unique(clip_frag_idx)
+        frag_attach_idx = bfs_edges[-1][1]
+        clip_map = []
+        for i in range(len(clip_frag_idx)):
+            clip_map.extend(self.dataset[index]['frags']['map'][clip_frag_idx[i]])
+        clip_map_attach = clip_map + self.dataset[index]['frags']['map'][frag_attach_idx]
+        #print('frag_node_map', self.dataset[index]['frags']['map'])
+
+        #get part mol
+        part_mol = Chem.RWMol(full_mol)
+        atom_idx = list(range(full_mol.GetNumAtoms()))
+        for i, atom in enumerate(full_mol.GetAtoms()):
+            if atom.GetSymbol() == 'H':
+                #remove if neighbor is not in clip_map
+                neighbor_atom = [atom.GetAtomMapNum() for atom in part_mol.GetAtomWithIdx(i).GetNeighbors()]
+                neighbor_map = [atom.GetAtomMapNum() for atom in part_mol.GetAtomWithIdx(i).GetNeighbors()][0]
+                if neighbor_map not in clip_map:
+                    atom_idx[i] = -1
+                if atom.GetAtomMapNum() not in clip_map:
+                    atom_idx[i] = -1
+            else:
+                if atom.GetAtomMapNum() not in clip_map:
+                    atom_idx[i] = -1
+        for i in range(len(atom_idx) - 1, -1, -1):
+            if atom_idx[i] == -1:
+                part_mol.RemoveAtom(i)
+        
+        frag_exp_link = []
+        for i in range(len(clip_frag_idx)):
+            for e_d, e in enumerate(bfs_edges_full):
+                if e[0] == clip_frag_idx[i] and e[1] not in clip_frag_idx:
+                    frag_exp_link.append(self.dataset[index]['frags']['links'][e_d][0])
+        for link_mp in frag_exp_link:
+            add_map = [i for i, atom in enumerate(part_mol.GetAtoms()) if atom.GetAtomMapNum() == link_mp][0]
+            part_mol.AddAtom(Chem.Atom(1))
+            part_mol.AddBond(add_map, part_mol.GetNumAtoms() - 1, Chem.rdchem.BondType.SINGLE)
+        part_mol = part_mol.GetMol()
+        part_mol = Chem.RemoveHs(part_mol)
+        part_mol = Chem.AddHs(part_mol, addCoords=True)
+
+
+        part_mol_atom_types = [atom.GetSymbol() for atom in part_mol.GetAtoms()]
+        part_mol_atom_coords = np.array([part_mol.GetConformer().GetAtomPosition(i) for i in range(part_mol.GetNumAtoms())])
+
+        #get part mol with attach
+        part_mol_attach = Chem.RWMol(full_mol)
+        atom_idx = list(range(full_mol.GetNumAtoms()))
+        for i, atom in enumerate(full_mol.GetAtoms()):
+            if full_mol.GetAtomWithIdx(i).GetSymbol() == 'H':
+                #remove if neighbor is not in clip_map
+                neighbor = [atom.GetAtomMapNum() for atom in part_mol_attach.GetAtomWithIdx(i).GetNeighbors()][0]
+                if neighbor not in clip_map_attach:
+                    atom_idx[i] = -1
+            else:
+                if atom.GetAtomMapNum() not in clip_map_attach:
+                    atom_idx[i] = -1
+        #print([i for i, idx in enumerate(atom_idx) if idx == -1])
+        for i in range(len(atom_idx) - 1, -1, -1):
+            if atom_idx[i] == -1:
+                part_mol_attach.RemoveAtom(i)
+        #if not self.check_leaf(edges, frag_attach_idx):
+        frag_exp_link = []
+        clip_frag_attach_idx = list(clip_frag_idx) + [frag_attach_idx]
+        for i in range(len(clip_frag_attach_idx)):
+            for e_d, e in enumerate(bfs_edges_full):
+                if e[0] == clip_frag_attach_idx[i] and e[1] not in clip_frag_attach_idx:
+                    frag_exp_link.append(self.dataset[index]['frags']['links'][e_d][0])
+        for link_mp in frag_exp_link:
+            add_map = [i for i, atom in enumerate(part_mol_attach.GetAtoms()) if atom.GetAtomMapNum() == link_mp][0]
+            part_mol_attach.AddAtom(Chem.Atom(1))
+            part_mol_attach.AddBond(add_map, part_mol_attach.GetNumAtoms() - 1, Chem.rdchem.BondType.SINGLE)
+            #print('add H atom symbol', part_mol_attach.GetAtomWithIdx(add_map).GetSymbol())
+        #else:
+        #    print('leaf node')
+        part_mol_attach = part_mol_attach.GetMol()
+        part_mol_attach = Chem.RemoveHs(part_mol_attach)
+        part_mol_attach = Chem.AddHs(part_mol_attach, addCoords=True)
+
+        part_mol_attach_atom_types = [atom.GetSymbol() for atom in part_mol_attach.GetAtoms()]
+        part_mol_attach_atom_coords = np.array([part_mol_attach.GetConformer().GetAtomPosition(i) for i in range(part_mol_attach.GetNumAtoms())])
+        '''
+        part_mol_atom = [self.dataset[index]['frags']['map'][e[0]] for e in bfs_edges] + \
+                        [self.dataset[index]['frags']['map'][e[1]] for e in bfs_edges[:-1]]
+        part_mol_atom = np.concatenate(part_mol_atom, axis=0)
+        part_mol_atom = np.unique(part_mol_atom)
+        part_mol_atom_types = self.dataset[index]['atom_types'][part_mol_atom]
+        part_mol_atom_coords = self.dataset[index]['atom_coords'][part_mol_atom]
+        '''
+
+        '''
+        #add focal atom
+        part_mol_atom_withfocal = [self.dataset[index]['frags']['map'][e[0]] for e in bfs_edges] + \
+                        [self.dataset[index]['frags']['map'][e[1]] for e in bfs_edges]
+        part_mol_atom_withfocal = np.concatenate(part_mol_atom_withfocal, axis=0)
+        part_mol_atom_withfocal = np.unique(part_mol_atom_withfocal)
+        part_mol_atom_types_withfocal = self.dataset[index]['atom_types'][part_mol_atom_withfocal]
+        part_mol_atom_coords_withfocal = self.dataset[index]['atom_coords'][part_mol_atom_withfocal]
+        '''
+        focal_atom_local = [i for i, atom in enumerate(part_mol.GetAtoms()) if atom.GetAtomMapNum() == self.dataset[index]['frags']['links'][clip_step - 1][0]][0]
+        focal_atom = [i for i, atom in enumerate(part_mol_attach.GetAtoms()) if atom.GetAtomMapNum() == self.dataset[index]['frags']['links'][clip_step - 1][0]][0]
+        #focal_atom = self.dataset[index]['frags']['map'][focal_frag][focal_atom_local]
+        
+        #frag
+        frag_mol_idx = self.dataset[index]['frags']['frag_idx'][attach_frag]
+        frag_mol = self.conf_vocab.conf[frag_mol_idx]
+
+        frag_mol = self.parse_frag_idx(frag_mol, self.dataset[index]['frags']['mol'], self.dataset[index]['frags']['map'][attach_frag])
+        frag_mol_data = self.parse_frag_mol(frag_mol)
+
+        if frag_mol is None:
+                print(frag_mol_idx, 'is None')
+
+        attach_atom_local = [i for i, atom in enumerate(frag_mol.GetAtoms()) if atom.GetAtomMapNum() == self.dataset[index]['frags']['links'][clip_step - 1][1]][0]
+        attach_atom = [i for i, atom in enumerate(part_mol_attach.GetAtoms()) if atom.GetAtomMapNum() == self.dataset[index]['frags']['links'][clip_step - 1][1]][0]
+        #attach_atom = self.dataset[index]['frags']['map'][bfs_edges[-1][1]][attach_atom_local]
+
+
+        #torsion angles
+        #prev_edge = bfs_edges[-1]
+        '''
+        prev_link_local = self.dataset[index]['frags']['link_atoms'][bfs_edges.index(prev_edge) - 1]
+        prev_link = (self.dataset[index]['frags']['map'][prev_edge[0]][prev_link_local[0]], 
+                self.dataset[index]['frags']['map'][prev_edge[1]][prev_link_local[1]])
+        index_rotate = [prev_link[1]] + self.dataset[index]['frags']['map'][focal_frag]
+        index_parent = np.concatenate([self.dataset[index]['frags']['map'][e[0]] for e in bfs_edges[:-1]], axis=0) + [prev_link[0]]
+        '''
+        index_rotate = [i for i, atom in enumerate(part_mol_attach.GetAtoms()) if atom.GetAtomMapNum() in self.dataset[index]['frags']['map'][attach_frag]]
+        index_rotate.remove(attach_atom)
+        index_rotate = [attach_atom] + index_rotate
+        index_parent = []
+        for e in bfs_edges[:-1]:
+            index_parent += self.dataset[index]['frags']['map'][e[0]]
+            index_parent += self.dataset[index]['frags']['map'][e[1]]
+        index_parent += self.dataset[index]['frags']['map'][focal_frag]
+        index_parent = list(np.unique(index_parent))
+        index_parent = [i for i, atom in enumerate(part_mol_attach.GetAtoms()) if atom.GetAtomMapNum() in index_parent]
+        index_parent.remove(focal_atom)
+        index_parent = index_parent + [focal_atom]
+        #add hydrogen
+        # get the hydrogen that connects to the rotate atoms
+        index_rotate_h = []
+        for i in index_rotate:
+            for j in part_mol_attach.GetAtomWithIdx(i).GetNeighbors():
+                if j.GetAtomicNum() == 1:
+                    index_rotate_h.append(j.GetIdx())
+        index_rotate += index_rotate_h
+        # get the hydrogen that connects to the parent atoms
+        index_parent_h = []
+        for i in index_parent:
+            for j in part_mol_attach.GetAtomWithIdx(i).GetNeighbors():
+                if j.GetAtomicNum() == 1:
+                    index_parent_h.append(j.GetIdx())
+        index_parent = index_parent_h + index_parent
+        coords_input_prev, torsion_output_prev = geom.change_torsion(part_mol_attach_atom_coords, [index_parent, index_rotate])
+        symmetric = (len(index_rotate) == 0 )
+
+        return {
+            'atom_types': part_mol_atom_types,
+            'atom_coords': part_mol_atom_coords,
+            'focal_atom_local': focal_atom_local,
+            'attach_atom_local': attach_atom_local,
+            'focal_atom': focal_atom,
+            'attach_atom': attach_atom,
+            'frag_atom_types': frag_mol_data['atom_types'],
+            'frag_atom_coords': frag_mol_data['atom_coords'],
+            'end': end,
+            'torsion_output_prev': torsion_output_prev,
+            'coords_input_prev': coords_input_prev,
+            'atom_types_withfocal': part_mol_attach_atom_types,
+            'pocket_atoms': pocket_atoms,
+            'pocket_coordinates': pocket_coordinates,
+            'first': False,
+            'symmetric': symmetric
+        }
+    
+    def __getitem__(self, index: int):
+        item = self.__cached_item__(index, self.epoch)
+        return item
+    

data/unimol/data/prepend_and_append_2d_dataset.py

@@ -0,0 +1,23 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+
+
+class PrependAndAppend2DDataset(BaseWrapperDataset):
+    def __init__(self, dataset, token=None):
+        super().__init__(dataset)
+        self.token = token
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, idx):
+        item = self.dataset[idx]
+        if self.token is not None:
+            h, w = item.size(-2), item.size(-1)
+            new_item = torch.full((h + 2, w + 2), self.token).type_as(item)
+            new_item[1:-1, 1:-1] = item
+            return new_item
+        return item

data/unimol/data/remove_hydrogen_dataset.py

@@ -0,0 +1,200 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+
+
+class DockRemoveHydrogenDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        atoms,
+        coordinates,
+        docked_coordinates,
+        remove_hydrogen=False,
+        remove_polar_hydrogen=False,
+    ):
+        self.dataset = dataset
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.docked_coordinates = docked_coordinates
+        self.remove_hydrogen = remove_hydrogen
+        self.remove_polar_hydrogen = remove_polar_hydrogen
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        atoms = dd[self.atoms]
+        coordinates = dd[self.coordinates]
+        docked = dd[self.docked_coordinates]
+
+        if self.remove_hydrogen:
+            mask_hydrogen = atoms != "H"
+            atoms = atoms[mask_hydrogen]
+            #print(coordinates.shape)
+            coordinates = coordinates[mask_hydrogen]
+            docked = docked[mask_hydrogen]
+        if not self.remove_hydrogen and self.remove_polar_hydrogen:
+            end_idx = 0
+            for i, atom in enumerate(atoms[::-1]):
+                if atom != "H":
+                    break
+                else:
+                    end_idx = i + 1
+            if end_idx != 0:
+                atoms = atoms[:-end_idx]
+                coordinates = coordinates[:-end_idx]
+                docked = docked[:-end_idx]
+        dd[self.atoms] = atoms
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        dd[self.docked_coordinates] = docked.astype(np.float32)
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+
+class RemoveHydrogenDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        atoms,
+        coordinates,
+        remove_hydrogen=False,
+        remove_polar_hydrogen=False,
+    ):
+        self.dataset = dataset
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.remove_hydrogen = remove_hydrogen
+        self.remove_polar_hydrogen = remove_polar_hydrogen
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        atoms = dd[self.atoms]
+        coordinates = dd[self.coordinates]
+
+        if self.remove_hydrogen:
+            mask_hydrogen = atoms != "H"
+            atoms = atoms[mask_hydrogen]
+            #print(coordinates.shape)
+            coordinates = coordinates[mask_hydrogen]
+        if not self.remove_hydrogen and self.remove_polar_hydrogen:
+            end_idx = 0
+            for i, atom in enumerate(atoms[::-1]):
+                if atom != "H":
+                    break
+                else:
+                    end_idx = i + 1
+            if end_idx != 0:
+                atoms = atoms[:-end_idx]
+                coordinates = coordinates[:-end_idx]
+        dd[self.atoms] = atoms
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class RemoveHydrogenResiduePocketDataset(BaseWrapperDataset):
+    def __init__(self, dataset, atoms, residues, coordinates, remove_hydrogen=True):
+        self.dataset = dataset
+        self.atoms = atoms
+        self.residues = residues
+        self.coordinates = coordinates
+        self.remove_hydrogen = remove_hydrogen
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        atoms = dd[self.atoms]
+        residues = dd[self.residues]
+        coordinates = dd[self.coordinates]
+        if len(atoms) != len(residues):
+            min_len = min(len(atoms), len(residues))
+            atoms = atoms[:min_len]
+            residues = residues[:min_len]
+            coordinates = coordinates[:min_len, :]
+
+        if self.remove_hydrogen:
+            mask_hydrogen = atoms != "H"
+            atoms = atoms[mask_hydrogen]
+            residues = residues[mask_hydrogen]
+            coordinates = coordinates[mask_hydrogen]
+
+        dd[self.atoms] = atoms
+        dd[self.residues] = residues
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class RemoveHydrogenPocketDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        atoms,
+        coordinates,
+        remove_hydrogen=True,
+        remove_polar_hydrogen=False,
+    ):
+        self.dataset = dataset
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.remove_hydrogen = remove_hydrogen
+        self.remove_polar_hydrogen = remove_polar_hydrogen
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        dd = self.dataset[index].copy()
+        atoms = dd[self.atoms]
+        coordinates = dd[self.coordinates]
+
+        if self.remove_hydrogen:
+            mask_hydrogen = atoms != "H"
+            atoms = atoms[mask_hydrogen]
+            coordinates = coordinates[mask_hydrogen]
+        if not self.remove_hydrogen and self.remove_polar_hydrogen:
+            end_idx = 0
+            for i, atom in enumerate(atoms[::-1]):
+                if atom != "H":
+                    break
+                else:
+                    end_idx = i + 1
+            if end_idx != 0:
+                atoms = atoms[:-end_idx]
+                coordinates = coordinates[:-end_idx]
+        dd[self.atoms] = atoms
+        dd[self.coordinates] = coordinates.astype(np.float32)
+        return dd
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)

data/unimol/data/resampling_dataset.py

@@ -0,0 +1,117 @@
+import logging
+
+import numpy as np
+
+from unicore.data  import BaseWrapperDataset
+from .plasma_utils import PlasmaArray
+
+logger = logging.getLogger(__name__)
+
+
+
+class ResamplingDataset(BaseWrapperDataset):
+    """Randomly samples from a given dataset at each epoch.
+    Sampling is done with or without replacement, depending on the "replace"
+    parameter.
+    Optionally, the epoch size can be rescaled. This is potentially desirable
+    to increase per-epoch coverage of the base dataset (since sampling with
+    replacement means that many items in the dataset will be left out). In the
+    case of sampling without replacement, size_ratio should be strictly less
+    than 1.
+    Args:
+        dataset (~torch.utils.data.Dataset): dataset on which to sample.
+        weights (List[float]): list of probability weights
+            (default: None, which corresponds to uniform sampling).
+        replace (bool): sampling mode; True for "with replacement", or False
+            for "without replacement" (default: True)
+        size_ratio (float): the ratio to subsample to; must be positive
+            (default: 1.0).
+        batch_by_size (bool): whether or not to batch by sequence length
+            (default: True).
+        seed (int): RNG seed to use (default: 0).
+        epoch (int): starting epoch number (default: 1).
+    """
+
+    def __init__(
+        self,
+        dataset,
+        replace=False,
+        size_ratio=1.0,
+        batch_by_size=True,
+        seed=0,
+        epoch=1,
+    ):
+        super().__init__(dataset)
+
+        
+
+        self.replace = replace
+
+        self.size_ratio = float(size_ratio)
+        self.actual_size = np.ceil(len(dataset) * self.size_ratio).astype(int)
+
+        self.seed = seed
+
+        self._cur_epoch = None
+        self._cur_indices = None
+
+        self.set_epoch(epoch)
+
+    def __getitem__(self, index):
+        return self.dataset[self._cur_indices.array[index]]
+
+    def __len__(self):
+        return self.actual_size
+
+    @property
+    def sizes(self):
+        if isinstance(self.dataset.sizes, list):
+            return [s[self._cur_indices.array] for s in self.dataset.sizes]
+        return self.dataset.sizes[self._cur_indices.array]
+
+    def num_tokens(self, index):
+        return self.dataset.num_tokens(self._cur_indices.array[index])
+
+    def size(self, index):
+        return self.dataset.size(self._cur_indices.array[index])
+
+    def ordered_indices(self):
+        return np.arange(len(self))
+
+    def prefetch(self, indices):
+        self.dataset.prefetch(self._cur_indices.array[indices])
+
+    @property
+    def can_reuse_epoch_itr_across_epochs(self):
+        return False
+
+    def set_epoch(self, epoch):
+        # log
+        
+
+        logger.info("ResamplingDataset.set_epoch: {}".format(epoch))
+        super().set_epoch(epoch)
+
+        if epoch == self._cur_epoch:
+            return
+
+        self._cur_epoch = epoch
+
+        # Generate a weighted sample of indices as a function of the
+        # random seed and the current epoch.
+
+        rng = np.random.RandomState(
+            [
+                42,  # magic number
+                self.seed % (2**32),  # global seed
+                self._cur_epoch,  # epoch index
+            ]
+        )
+        self._cur_indices = PlasmaArray(rng.choice(
+                len(self.dataset),
+                self.actual_size,
+                replace=self.replace,
+                p=(None),
+            )
+        )
+        #print(self._cur_epoch, self._cur_indices.array[:10])

data/unimol/data/tta_dataset.py

@@ -0,0 +1,146 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import numpy as np
+from functools import lru_cache
+from unicore.data import BaseWrapperDataset
+
+
+class TTADataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, coordinates, conf_size=10):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.conf_size = conf_size
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    def __len__(self):
+        return len(self.dataset) * self.conf_size
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        smi_idx = index // self.conf_size
+        coord_idx = index % self.conf_size
+        atoms = np.array(self.dataset[smi_idx][self.atoms])
+        coordinates = np.array(self.dataset[smi_idx][self.coordinates][coord_idx])
+        smi = self.dataset[smi_idx]["smi"]
+        target = self.dataset[smi_idx]["target"]
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "smi": smi,
+            "target": target,
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+class TTADecoderDataset(BaseWrapperDataset):
+    def __init__(self, dataset, seed, atoms, coordinates, selfies="selfies", conf_size=10):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.selfies = selfies
+        self.conf_size = conf_size
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    def __len__(self):
+        return len(self.dataset) * self.conf_size
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        smi_idx = index // self.conf_size
+        coord_idx = index % self.conf_size
+        atoms = np.array(self.dataset[smi_idx][self.atoms])
+        coordinates = np.array(self.dataset[smi_idx][self.coordinates][coord_idx])
+        selfies = np.array(self.dataset[smi_idx][self.selfies])
+        smi = self.dataset[smi_idx]["smi"]
+        target = self.dataset[smi_idx]["target"]
+        return {
+            "atoms": atoms,
+            "selfies": selfies,
+            "coordinates": coordinates.astype(np.float32),
+            "smi": smi,
+            "target": target,
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+class TTADockingPoseDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        atoms,
+        coordinates,
+        pocket_atoms,
+        pocket_coordinates,
+        holo_coordinates,
+        holo_pocket_coordinates,
+        is_train=True,
+        conf_size=10,
+    ):
+        self.dataset = dataset
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.holo_coordinates = holo_coordinates
+        self.holo_pocket_coordinates = holo_pocket_coordinates
+        self.is_train = is_train
+        self.conf_size = conf_size
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+
+    def __len__(self):
+        return len(self.dataset) * self.conf_size
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        smi_idx = index // self.conf_size
+        coord_idx = index % self.conf_size
+        atoms = np.array(self.dataset[smi_idx][self.atoms])
+        coordinates = np.array(self.dataset[smi_idx][self.coordinates][coord_idx])
+        pocket_atoms = np.array(
+            [item[0] for item in self.dataset[smi_idx][self.pocket_atoms]]
+        )
+        pocket_coordinates = np.array(self.dataset[smi_idx][self.pocket_coordinates][0])
+        if self.is_train:
+            holo_coordinates = np.array(self.dataset[smi_idx][self.holo_coordinates][0])
+            holo_pocket_coordinates = np.array(
+                self.dataset[smi_idx][self.holo_pocket_coordinates][0]
+            )
+        else:
+            holo_coordinates = coordinates
+            holo_pocket_coordinates = pocket_coordinates
+
+        smi = self.dataset[smi_idx]["smi"]
+        pocket = self.dataset[smi_idx]["pocket"]
+
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_coordinates": holo_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": holo_pocket_coordinates.astype(np.float32),
+            "smi": smi,
+            "pocket": pocket,
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)

data/unimol/data/vae_binding_dataset.py

@@ -0,0 +1,176 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+from functools import lru_cache
+
+import numpy as np
+from unicore.data import BaseWrapperDataset
+
+from . import data_utils
+
+
+class VAEBindingDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        pocket_atoms,
+        pocket_coordinates,
+        selfies,
+        is_train=True,
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.selfies = selfies
+        self.is_train = is_train
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        coordinates = self.dataset[index][self.coordinates]
+        pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index][self.pocket_atoms]]
+        )
+        pocket_coordinates = np.stack(self.dataset[index][self.pocket_coordinates])
+
+        smi = self.dataset[index]["smi"]
+        pocket = self.dataset[index]["pocket"]
+        #affinity = self.dataset[index][self.affinity]
+        selfies = np.array(self.dataset[index][self.selfies])
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "holo_coordinates": coordinates.astype(np.float32),#placeholder
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": pocket_coordinates.astype(np.float32),#placeholder
+            "smi": smi,
+            "pocket": pocket,
+            "selfies": selfies
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+
+class VAEBindingTestDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        atoms,
+        coordinates,
+        pocket_atoms,
+        pocket_coordinates,
+        is_train=True,
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.atoms = atoms
+        self.coordinates = coordinates
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.is_train = is_train
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        atoms = np.array(self.dataset[index][self.atoms])
+        coordinates = self.dataset[index][self.coordinates]
+        pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index][self.pocket_atoms]]
+        )
+        pocket_coordinates = np.stack(self.dataset[index][self.pocket_coordinates])
+
+        smi = self.dataset[index]["smi"]
+        pocket = self.dataset[index]["pocket_name"]
+        lig = self.dataset[index]["lig_name"]
+        #affinity = self.dataset[index][self.affinity]
+        return {
+            "atoms": atoms,
+            "coordinates": coordinates.astype(np.float32),
+            "holo_coordinates": coordinates.astype(np.float32),#placeholder
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": pocket_coordinates.astype(np.float32),#placeholder
+            "smi": smi,
+            "pocket": pocket,
+            "lig": lig
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+class VAEGenerationTestDataset(BaseWrapperDataset):
+    def __init__(
+        self,
+        dataset,
+        seed,
+        pocket_atoms,
+        pocket_coordinates,
+        is_train=True,
+    ):
+        self.dataset = dataset
+        self.seed = seed
+        self.pocket_atoms = pocket_atoms
+        self.pocket_coordinates = pocket_coordinates
+        self.is_train = is_train
+        self.set_epoch(None)
+
+    def set_epoch(self, epoch, **unused):
+        super().set_epoch(epoch)
+        self.epoch = epoch
+    
+    def pocket_atom(self, atom):
+        if atom[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']:
+            return atom[1]
+        else:
+            return atom[0]
+
+    @lru_cache(maxsize=16)
+    def __cached_item__(self, index: int, epoch: int):
+        pocket_atoms = np.array(
+            [self.pocket_atom(item) for item in self.dataset[index][self.pocket_atoms]]
+        )
+        pocket_coordinates = np.stack(self.dataset[index][self.pocket_coordinates])
+
+        
+        return {
+            "pocket_atoms": pocket_atoms,
+            "pocket_coordinates": pocket_coordinates.astype(np.float32),
+            "holo_pocket_coordinates": pocket_coordinates.astype(np.float32),#placeholder
+        }
+
+    def __getitem__(self, index: int):
+        return self.__cached_item__(index, self.epoch)
+
+

data/unimol/demo.py

@@ -0,0 +1,12 @@
+from unicore.data import LMDBDataset
+
+path = "./data/DUD-E/raw/all/ada17/mols.lmdb"  # 换成一个具体 target
+print("open", path)
+ds = LMDBDataset(path)
+print("len =", len(ds))
+
+for i in range(len(ds)):
+    if i % 1000 == 0:
+        print("index", i, "...")
+    ds[i]   # 只要能成功 __getitem__ 就行
+print("done")

data/unimol/encode_pocket_ligand.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python3 -u
+# Copyright (c) DP Techonology, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+import pickle
+import torch
+from unicore import checkpoint_utils, distributed_utils, options, utils
+from unicore.logging import progress_bar
+from unicore import tasks
+import numpy as np
+from tqdm import tqdm
+import unicore
+import pandas as pd
+
+logging.basicConfig(
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+    stream=sys.stdout,
+)
+logger = logging.getLogger("unimol.inference")
+
+
+def main(args):
+
+    use_fp16 = args.fp16
+    use_cuda = torch.cuda.is_available() and not args.cpu
+
+    if use_cuda:
+        torch.cuda.set_device(args.device_id)
+
+
+    # Load model
+    logger.info("loading model(s) from {}".format(args.path))
+    state = checkpoint_utils.load_checkpoint_to_cpu(args.path)
+    task = tasks.setup_task(args)
+    model = task.build_model(args)
+    model.load_state_dict(state["model"], strict=False)
+
+    # Move models to GPU
+    if use_fp16:
+        model.half()
+    if use_cuda:
+        model.cuda()
+
+    # Print args
+    logger.info(args)
+
+
+    model.eval()
+    
+    root_dir = args.save_dir
+
+    # all_mols = []
+    # for dirpath, dirnames, filenames in os.walk(root_dir):
+    #     for f in filenames:
+    #         if f.endswith('_mols.lmdb'):
+    #             all_mols.append(os.path.join(dirpath, f))
+
+    # for mol_path in tqdm(all_mols, desc="Encoding pocket-ligand pairs"):
+    #     mol_files = [f for f in filenames if f.endswith('_mols.lmdb')]
+
+    #     dirpath = os.path.dirname(mol_path)
+    #     mol_file = os.path.basename(mol_path)
+    #     prefix = mol_file.replace('_mols.lmdb', '')
+    #     pocket_file = prefix + '_pocket10_pocket.lmdb'
+    #     pocket_path = os.path.join(dirpath, pocket_file)
+
+    #     try:
+    #         # Encode ligand + pocket
+    #         ligand_emb, pocket_emb = task.encode_pocket_and_ligand(
+    #             model,
+    #             mol_path,
+    #             pocket_path,
+    #         )
+    #     except Exception as e:
+    #         logger.error(f"Error encoding {mol_path} / {pocket_path}: {e}")
+    #         continue
+        
+    #     save_dir = dirpath
+    #     os.makedirs(save_dir, exist_ok=True)
+
+    #     lig_path = os.path.join(save_dir, mol_file.replace('_mols.lmdb', '') +  "_ligand_emb.pt")
+    #     pkt_path = os.path.join(save_dir, pocket_file.replace('_pocket.lmdb', '') + "_pocket_emb.pt")
+
+    #     torch.save(ligand_emb, lig_path)
+    #     torch.save(pocket_emb, pkt_path)
+    ligand_lmdbs = []
+    for dirpath, dirnames, filenames in os.walk(root_dir):
+        for f in filenames:
+            if f.endswith(".lmdb"):
+                ligand_lmdbs.append(os.path.join(dirpath, f))
+    logger.info(f"Found {len(ligand_lmdbs)} ligand lmdb files under {root_dir}")
+    for lig_path in tqdm(ligand_lmdbs, desc="Encoding ligands"):
+        try:
+            ligand_emb = task.encode_ligand_cls(
+                model,
+                lig_path,
+            )
+        except Exception as e:
+            logger.error(f"Error encoding ligand {lig_path}: {e}")
+            continue
+
+        save_dir = os.path.dirname(lig_path)
+        os.makedirs(save_dir, exist_ok=True)
+
+        prefix = os.path.splitext(os.path.basename(lig_path))[0]
+        out_path = os.path.join(save_dir, prefix + "_ligand_emb.pt")
+
+        torch.save(ligand_emb, out_path)
+        logger.info(f"Saved ligand embedding to {out_path}")
+
+
+
+def cli_main():
+    # add args
+    parser = options.get_validation_parser()
+    parser.add_argument("--save-dir", type=str, default="", help="path for saved embedding data")
+    options.add_model_args(parser)
+    args = options.parse_args_and_arch(parser)
+
+    distributed_utils.call_main(args, main)
+
+
+if __name__ == "__main__":
+    cli_main()

data/unimol/losses/__init__.py

@@ -0,0 +1,7 @@
+from pathlib import Path
+import importlib
+
+# automatically import any Python files in the criterions/ directory
+for file in sorted(Path(__file__).parent.glob("*.py")):
+    if not file.name.startswith("_"):
+        importlib.import_module("unimol.losses." + file.name[:-3])

data/unimol/losses/cross_entropy.py

@@ -0,0 +1,1333 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import torch
+import torch.nn.functional as F
+import pandas as pd
+from unicore import metrics
+from unicore.losses import UnicoreLoss, register_loss
+from unicore.losses.cross_entropy import CrossEntropyLoss
+from sklearn.metrics import roc_auc_score, precision_score, recall_score, f1_score
+import numpy as np
+import warnings
+from sklearn.metrics import top_k_accuracy_score
+from rdkit.ML.Scoring.Scoring import CalcBEDROC
+import scipy.stats as stats
+
+
+def calculate_bedroc(y_true, y_score, alpha):
+    """
+    Calculate BEDROC score.
+
+    Parameters:
+    - y_true: true binary labels (0 or 1)
+    - y_score: predicted scores or probabilities
+    - alpha: parameter controlling the degree of early retrieval emphasis
+
+    Returns:
+    - BEDROC score
+    """
+    
+        # concate res_single and labels
+    scores = np.expand_dims(y_score, axis=1)
+    y_true = np.expand_dims(y_true, axis=1)
+    #print(scores.shape, y_true.shape)
+    scores = np.concatenate((scores, y_true), axis=1)
+    # inverse sort scores based on first column
+    scores = scores[scores[:,0].argsort()[::-1]]
+    bedroc = CalcBEDROC(scores, 1, 80.5)
+    return bedroc
+
+@register_loss("decoder_loss")
+class DecoderLoss(CrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+
+    def forward(self, model, sample, reduce=True, fix_encoder=False):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        net_output = model(
+            **sample["net_input"],
+            features_only=True,
+            classification_head_name=None,
+            fix_encoder=fix_encoder
+        )
+        loss = self.compute_loss(model, net_output, sample, reduce=reduce)
+        targets = sample["net_input"]["selfie_tokens"]
+        sample_size = targets.size(0)
+        
+        lprobs = net_output[:,:,:targets.shape[-1]]
+        if not self.training:
+            logging_output = {
+                "loss": loss.data,
+                "prob": lprobs.data,
+                "target": targets.data,
+                "smi_name": sample["smi_name"],
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        return loss, sample_size, logging_output
+
+    def compute_loss(self, model, net_output, sample, reduce=True):
+        lprobs = net_output
+        targets = sample["net_input"]["selfie_tokens"]
+        lprobs = lprobs[:,:,:targets.shape[-1]]
+        # print("111", lprobs.shape, targets.shape, sample["target"]["finetune_target"].shape)
+        nll_loss = F.nll_loss(
+            lprobs,
+            targets,
+            reduction="sum" if reduce else "none",
+        ) / lprobs.shape[-1]
+
+        loss =  nll_loss 
+        #print(loss.data, nll_loss.data, kld_loss.data)
+        return loss
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        # if split == "valid":
+        #     print("hi1")
+        loss = sum(log.get("loss", 0).float() for log in logging_outputs)
+        # if split == "valid":
+        #     print("hi2")
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # if split == "valid":
+        #     print("hi3")
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss_all", loss / sample_size / math.log(2), sample_size, round=3
+        )
+
+        if "valid" in split or "test" in split:
+            
+            prob_list = []
+            pred_list = []
+            target_list = []
+            for log in logging_outputs:
+                prob = log.get("prob")
+                prob = torch.transpose(prob, 1, 2)
+                prob = prob.reshape((-1, prob.shape[-1]))
+                prob_list.append(prob)
+                pred = log.get("prob").argmax(dim=1)
+                pred = pred.flatten()
+                pred_list.append(pred)
+                target = log.get("target")
+                target = target.flatten()
+                target_list.append(target)
+
+            preds = torch.cat(pred_list, dim=0)
+            targets = torch.cat(target_list, dim=0)
+            #print(preds.shape, targets.shape)
+            acc = (preds == targets).float().mean(dim=-1)
+            #print(acc.shape)
+            metrics.log_scalar(
+                f"{split}_acc", acc , sample_size, round=3
+            )
+            
+        
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return is_train
+
+
+@register_loss("decoder_vae_loss")
+class DecoderVAELoss(CrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+
+    def forward(self, model, sample, candidate_reps, candidate_embs, candidate_smiles, reduce=True, fix_encoder=False):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        net_output = model(
+            **sample["net_input"],
+            candidate_reps=candidate_reps,
+            candidate_embs=candidate_embs,
+            candidate_smiles=candidate_smiles,
+            features_only=True,
+            classification_head_name=None,
+            fix_encoder=fix_encoder
+        )
+        loss, nll_loss, kld_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
+        targets = sample["net_input"]["selfie_tokens"]
+        sample_size = targets.size(0)
+        
+        lprobs = net_output[0][:,:,:targets.shape[-1]]
+        if not self.training:
+            logging_output = {
+                "loss": loss.data,
+                "kld_loss": kld_loss.data,
+                "nll_loss": nll_loss.data,
+                "prob": lprobs.data,
+                "target": targets.data,
+                "smi_name": sample["smi_name"],
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "kld_loss": kld_loss.data,
+                "nll_loss": nll_loss.data,
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        return loss, sample_size, logging_output
+
+    def compute_loss(self, model, net_output, sample, reduce=True):
+        out, z, mu, log_var = net_output
+        lprobs = out
+        targets = sample["net_input"]["selfie_tokens"]
+        lprobs = lprobs[:,:,:targets.shape[-1]]
+        # print("111", lprobs.shape, targets.shape, sample["target"]["finetune_target"].shape)
+        nll_loss = F.nll_loss(
+            lprobs,
+            targets,
+            reduction="sum" if reduce else "none",
+        ) / lprobs.shape[-1]
+        kld_loss = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp()) / lprobs.shape[1]
+        p=0.2
+        loss = p * kld_loss + (1-p) * nll_loss 
+        #print(loss.data, nll_loss.data, kld_loss.data)
+        return loss, nll_loss, kld_loss
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        # if split == "valid":
+        #     print("hi1")
+        loss_all = sum(log.get("loss", 0).float() for log in logging_outputs)
+        loss_kld = sum(log.get("kld_loss", 0).float() for log in logging_outputs)
+        loss_nll = sum(log.get("nll_loss", 0).float() for log in logging_outputs)
+        # if split == "valid":
+        #     print("hi2")
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # if split == "valid":
+        #     print("hi3")
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss_all", loss_all / sample_size / math.log(2), sample_size, round=3
+        )
+        metrics.log_scalar(
+            "loss_kld", loss_kld / sample_size, sample_size, round=3
+        )
+        metrics.log_scalar(
+            "loss_nll", loss_nll / sample_size, sample_size, round=3
+        )
+        if "valid" in split or "test" in split:
+            
+            prob_list = []
+            pred_list = []
+            target_list = []
+            for log in logging_outputs:
+                prob = log.get("prob")
+                prob = torch.transpose(prob, 1, 2)
+                prob = prob.reshape((-1, prob.shape[-1]))
+                prob_list.append(prob)
+                pred = log.get("prob").argmax(dim=1)
+                pred = pred.flatten()
+                pred_list.append(pred)
+                target = log.get("target")
+                target = target.flatten()
+                target_list.append(target)
+
+            probs = torch.cat(prob_list, dim=0)
+            preds = torch.cat(pred_list, dim=0)
+            targets = torch.cat(target_list, dim=0)
+            #print(preds.shape, targets.shape)
+            acc = (preds == targets).float().mean(dim=-1)
+            #print(acc.shape)
+            metrics.log_scalar(
+                f"{split}_acc", acc , sample_size, round=3
+            )
+            '''
+            # smi_list = [
+            #     item for log in logging_outputs for item in log.get("smi_name")
+            # ]
+            probs = torch.exp(probs)
+            #prob_flat = prob_flat.reshape((-1, prob_flat.shape[-1]))
+            print(probs.shape)
+
+            #targets = targets.squeeze(dim=-1)
+            auc = roc_auc_score(targets.cpu(), probs.cpu(), multi_class="ovo", labels=torch.arange(probs.shape[-1]))
+            #df = df.groupby("smi").mean()
+            #agg_auc = roc_auc_score(df["targets"], df["probs"])
+            agg_auc = auc
+            
+            metrics.log_scalar(f"{split}_auc", auc, sample_size, round=3)
+            metrics.log_scalar(f"{split}_agg_auc", agg_auc, sample_size, round=4)
+            '''
+        
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return is_train
+
+
+@register_loss("finetune_cross_entropy")
+class FinetuneCrossEntropyLoss(CrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+
+    def forward(self, model, sample, reduce=True, fix_encoder=False):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        net_output = model(
+            **sample["net_input"],
+            features_only=True,
+            classification_head_name=self.args.classification_head_name,
+            fix_encoder=fix_encoder
+        )
+        logit_output = net_output[0]
+        loss = self.compute_loss(model, logit_output, sample, reduce=reduce)
+        sample_size = sample["target"]["finetune_target"].size(0)
+        if not self.training:
+            probs = F.softmax(logit_output.float(), dim=-1).view(
+                -1, logit_output.size(-1)
+            )
+            logging_output = {
+                "loss": loss.data,
+                "prob": probs.data,
+                "target": sample["target"]["finetune_target"].view(-1).data,
+                "smi_name": sample["smi_name"],
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        return loss, sample_size, logging_output
+
+    def compute_loss(self, model, net_output, sample, reduce=True):
+        lprobs = F.log_softmax(net_output.float(), dim=-1)
+        lprobs = lprobs.view(-1, lprobs.size(-1))
+        targets = sample["target"]["finetune_target"].view(-1)
+        # print("111", lprobs.shape, targets.shape, sample["target"]["finetune_target"].shape)
+        loss = F.nll_loss(
+            lprobs,
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        return loss
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
+        )
+        if "valid" in split or "test" in split:
+            acc_sum = sum(
+                sum(log.get("prob").argmax(dim=-1) == log.get("target"))
+                for log in logging_outputs
+            )
+            probs = torch.cat([log.get("prob") for log in logging_outputs], dim=0)
+            metrics.log_scalar(
+                f"{split}_acc", acc_sum / sample_size, sample_size, round=3
+            )
+            if probs.size(-1) == 2:
+                # binary classification task, add auc score
+                targets = torch.cat(
+                    [log.get("target", 0) for log in logging_outputs], dim=0
+                )
+                smi_list = [
+                    item for log in logging_outputs for item in log.get("smi_name")
+                ]
+                df = pd.DataFrame(
+                    {
+                        "probs": probs[:, 1].cpu(),
+                        "targets": targets.cpu(),
+                        "smi": smi_list,
+                    }
+                )
+                auc = roc_auc_score(df["targets"], df["probs"])
+                df = df.groupby("smi").mean()
+                agg_auc = roc_auc_score(df["targets"], df["probs"])
+                metrics.log_scalar(f"{split}_auc", auc, sample_size, round=3)
+                metrics.log_scalar(f"{split}_agg_auc", agg_auc, sample_size, round=4)
+
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return is_train
+
+@register_loss("ce")
+class CEntropyLoss(CrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+
+    def forward(self, model, sample, reduce=True, fix_encoder=False):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        net_output = model(
+            **sample["net_input"],
+            smi_list = sample["smi_name"],
+            pocket_list = sample["pocket_name"],
+            features_only=True,
+            fix_encoder=fix_encoder
+        )
+        logit_output = net_output
+        loss = self.compute_loss(model, logit_output, sample, reduce=reduce)
+        #print(sample["target"]["finetune_target"])
+        sample_size = sample["target"]["finetune_target"].size(0)
+        if not self.training:
+            probs = torch.sigmoid(logit_output.float())
+            logging_output = {
+                "loss": loss.data,
+                "prob": probs.data,
+                "target": sample["target"]["finetune_target"].view(-1).data,
+                "smi_name": sample["smi_name"],
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        return loss, sample_size, logging_output
+
+    def compute_loss(self, model, net_output, sample, reduce=True):
+
+        targets = sample["target"]["finetune_target"].view(-1)
+        # print("111", lprobs.shape, targets.shape, sample["target"]["finetune_target"].shape)
+        #print(net_output.shape, targets.shape)
+        loss = F.binary_cross_entropy_with_logits(
+            net_output.float(),
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        return loss
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
+        )
+        if "valid" in split or "test" in split:
+
+            # get acc
+            acc_sum = 0
+            for log in logging_outputs:
+                probs = torch.sigmoid(log.get("prob"))
+                targets = log.get("target")
+                probs = probs > 0.5
+                # convert to int
+                probs = probs.long()
+            
+            
+            logs = [log.get("prob") for log in logging_outputs]
+            targets = torch.cat(
+                [log.get("target", 0) for log in logging_outputs], dim=0
+            )
+            probs = torch.cat([log.get("prob") for log in logging_outputs], dim=0)
+            #probs = torch.sigmoid(probs)
+            print(probs.shape, targets.shape)
+            print(probs[:10], targets[:10])
+            preds = probs > 0.5
+            # convert to int
+            preds = preds.long()
+            acc_sum = (preds == targets).sum()
+            metrics.log_scalar(
+                f"{split}_acc", acc_sum / sample_size, sample_size, round=3
+            )
+            # binary classification task, add auc score
+            
+            
+            smi_list = [
+                item for log in logging_outputs for item in log.get("smi_name")
+            ]
+            df = pd.DataFrame(
+                {
+                    "probs": probs.cpu(),
+                    "targets": targets.cpu(),
+                    "smi": smi_list,
+                }
+            )
+            # get values of df["targets"]
+            # 
+            
+            auc = roc_auc_score(df["targets"].values, df["probs"].values)
+            df = df.groupby("smi").mean()
+            agg_auc = roc_auc_score(df["targets"], df["probs"])
+            #print(df["targets"])
+            metrics.log_scalar(f"{split}_auc", auc, sample_size, round=3)
+            metrics.log_scalar(f"{split}_agg_auc", agg_auc, sample_size, round=4)
+
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return is_train
+
+
+@register_loss("in_batch_softmax")
+class IBSLoss(CrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+
+    def set_num_updates(self, num_updates):
+        self.num_updates = 0
+        self.num_updates = num_updates
+
+    def forward(self, model, sample, reduce=True, fix_encoder=False):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        net_output = model(
+            **sample["net_input"],
+            smi_list = sample["smi_name"],
+            pocket_list = sample["pocket_name"],
+            features_only=True,
+            fix_encoder=fix_encoder,
+            is_train = self.training
+        )
+        
+        logit_output = net_output[0]
+        loss_diff,_,_ = net_output[2]
+        loss = self.compute_loss(model, logit_output, sample, reduce=reduce)
+        #print(loss.item(), loss_diff.item())  15:1.9
+        loss = loss + 0.5 * loss_diff
+        sample_size = logit_output.size(0)
+        targets = torch.arange(sample_size, dtype=torch.long).cuda()
+        affinities = sample["target"]["finetune_target"].view(-1)
+        if not self.training:
+            logit_output = logit_output[:,:sample_size]
+            probs = F.softmax(logit_output.float(), dim=-1).view(
+                -1, logit_output.size(-1)
+            )
+            logging_output = {
+                "loss": loss.data,
+                "prob": probs.data,
+                "target": targets,
+                "smi_name": sample["smi_name"],
+                "sample_size": sample_size,
+                "bsz": targets.size(0),
+                "scale": net_output[1].data,
+                "affinity": affinities,
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "sample_size": sample_size,
+                "bsz": targets.size(0),
+                "scale": net_output[1].data
+            }
+        return loss, sample_size, logging_output
+
+    def compute_loss(self, model, net_output, sample, reduce=True):
+        logits = net_output.float()
+        B = logits.size(0)
+        lprobs_pocket = F.log_softmax(logits, dim=-1)
+        lprobs_pocket = lprobs_pocket.view(-1, lprobs_pocket.size(-1))
+        sample_size = lprobs_pocket.size(0)
+        targets= torch.arange(sample_size, dtype=torch.long).view(-1).cuda()
+
+        # pocket retrieve mol
+        loss_pocket = F.nll_loss(
+            lprobs_pocket,
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        
+        logits_mol_side = logits[:, :B] 
+        lprobs_mol = F.log_softmax(torch.transpose(logits_mol_side, 0, 1), dim=-1)
+        lprobs_mol = lprobs_mol.view(-1, lprobs_mol.size(-1))
+        lprobs_mol = lprobs_mol[:sample_size]
+
+        # mol retrieve pocket
+        loss_mol = F.nll_loss(
+            lprobs_mol,
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        
+        loss = 0.5 * loss_pocket + 0.5 * loss_mol
+        return loss
+    
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        metrics.log_scalar("scale", logging_outputs[0].get("scale"), round=3)
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
+        )
+        if "valid" in split or "test" in split:
+            acc_sum = sum(
+                sum(log.get("prob").argmax(dim=-1) == log.get("target"))
+                for log in logging_outputs
+            )
+            
+            prob_list = []
+            if len(logging_outputs) == 1:
+                prob_list.append(logging_outputs[0].get("prob"))
+            else:
+                for i in range(len(logging_outputs)-1):
+                    prob_list.append(logging_outputs[i].get("prob"))
+            probs = torch.cat(prob_list, dim=0)
+            
+            metrics.log_scalar(
+                f"{split}_acc", acc_sum / sample_size, sample_size, round=3
+            )
+
+            metrics.log_scalar(
+                "valid_neg_loss", -loss_sum / sample_size / math.log(2), sample_size, round=3
+            )
+            
+            
+            targets = torch.cat(
+                [log.get("target", 0) for log in logging_outputs], dim=0
+            )
+            print(targets.shape, probs.shape)
+
+            targets = targets[:len(probs)]
+            bedroc_list = []
+            auc_list = []
+            for i in range(len(probs)):
+                prob = probs[i]
+                target = targets[i]
+                label = torch.zeros_like(prob)
+                label[target]=1.0
+                cur_auc = roc_auc_score(label.cpu(), prob.cpu())
+                auc_list.append(cur_auc)
+                bedroc = calculate_bedroc(label.cpu(), prob.cpu(), 80.5)
+                bedroc_list.append(bedroc)
+            bedroc = np.mean(bedroc_list)
+            auc = np.mean(auc_list)
+            
+            top_k_acc = top_k_accuracy_score(targets.cpu(), probs.cpu(), k=3, normalize=True)
+            metrics.log_scalar(f"{split}_auc", auc, sample_size, round=3)
+            metrics.log_scalar(f"{split}_bedroc", bedroc, sample_size, round=3)
+            metrics.log_scalar(f"{split}_top3_acc", top_k_acc, sample_size, round=3)
+
+            
+
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return is_train
+
+
+@register_loss("in_batch_softmax_hinge")
+class IBSLoss_rank(CrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+        self.lambda_hinge = 1.5
+        self.num_updates = 0
+        self.margin = 0.0
+    
+    def set_num_updates(self, num_updates):
+        self.num_updates = num_updates
+
+    def forward(self, model, sample, reduce=True, fix_encoder=False):
+        """Compute the loss for the given sample.
+        """
+        net_output = model(
+            **sample["net_input"],
+            smi_list = sample["smi_name"],
+            pocket_list = sample["pocket_name"],
+            features_only=True,
+            fix_encoder=fix_encoder,
+            is_train = self.training
+        )
+        
+        logit_output = net_output[0]
+        loss_diff,_,_ = net_output[2]
+
+        extra = net_output[3]
+        loss = self.compute_loss(model, logit_output, extra, sample, reduce=reduce)
+        #print(loss.item(), loss_diff.item())  15:1.9
+        loss = loss + 0.5 * loss_diff
+        sample_size = logit_output.size(0)
+        targets = torch.arange(sample_size, dtype=torch.long).cuda()
+        affinities = sample["target"]["finetune_target"].view(-1)
+        if not self.training:
+            logit_output = logit_output[:,:sample_size]
+            probs = F.softmax(logit_output.float(), dim=-1).view(
+                -1, logit_output.size(-1)
+            )
+            logging_output = {
+                "loss": loss.data,
+                "prob": probs.data,
+                "target": targets,
+                "smi_name": sample["smi_name"],
+                "sample_size": sample_size,
+                "bsz": targets.size(0),
+                "scale": net_output[1].data,
+                "affinity": affinities,
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "sample_size": sample_size,
+                "bsz": targets.size(0),
+                "scale": net_output[1].data
+            }
+        return loss, sample_size, logging_output
+
+    def compute_loss(self, model, net_output, extra, sample, reduce=True):
+        logits = net_output.float()
+        B = logits.size(0)
+        lprobs_pocket = F.log_softmax(logits, dim=-1)
+        lprobs_pocket = lprobs_pocket.view(-1, lprobs_pocket.size(-1))
+        sample_size = lprobs_pocket.size(0)
+        targets= torch.arange(sample_size, dtype=torch.long).view(-1).cuda()
+
+        # pocket retrieve mol
+        loss_pocket = F.nll_loss(
+            lprobs_pocket,
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        
+        logits_mol_side = logits[:, :B] 
+        lprobs_mol = F.log_softmax(torch.transpose(logits_mol_side, 0, 1), dim=-1)
+        lprobs_mol = lprobs_mol.view(-1, lprobs_mol.size(-1))
+        lprobs_mol = lprobs_mol[:sample_size]
+
+        # mol retrieve pocket
+        loss_mol = F.nll_loss(
+            lprobs_mol,
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        
+        loss = 0.5 * loss_pocket + 0.5 * loss_mol
+
+        ##====
+        lig_emb_aux = torch.cat([extra['mol_emb'].detach(), extra['decoy_emb']], dim=0)   # [2B, D]
+        logit_lig = torch.matmul(lig_emb_aux, lig_emb_aux.t())    
+        hard_idx = torch.arange(B, device=logits.device)
+        hard_neg = logit_lig[hard_idx, hard_idx + B]
+
+        # row = logit_lig[hard_idx, :].clone()      # [B, 2B]
+        # row[hard_idx, hard_idx] = 0.0
+        # row[hard_idx, hard_idx + B] = 0.0
+        # avg_neg = (row.sum(dim=-1) / (2*B - 2)).detach()
+
+        row = logit_lig[hard_idx, :B].clone()      # [B, 2B]
+        row[hard_idx, hard_idx] = 0.0
+        avg_neg = (row.sum(dim=-1) / (B - 1)).detach()
+        #avg_neg = logit_lig[hard_idx, :].mean(dim=-1).detach()
+        
+        # 0: is hard, 1: is random
+        hir = sample["hard_label"].view(-1).to(logits.device).float()  # 0/1
+        mask = (1.0 - hir)
+        loss_mean = F.relu(avg_neg - hard_neg) * mask
+        loss_mean = loss_mean.sum() if reduce else loss_mean
+        # with torch.no_grad():
+        #     mask = (1.0 - hir)
+        #     gap = (avg_neg - hard_neg + self.margin)
+        #     vr_all = (gap > 0).float().mean()
+        #     vr_true = ((gap > 0) & (mask > 0)).float().sum() / mask.sum().clamp_min(1.0)
+        #     print(
+        #         "loss_aux", loss_mean.item(),                  
+        #         "avg", avg_neg.mean().item(),
+        #         "hard", hard_neg.mean().item(),
+        #         "gap", gap.mean().item(),                       
+        #         "vr_all", vr_all.item(),
+        #         "vr_true", vr_true.item(),
+        #         "hir", hir.mean().item(),
+        #         "n_true", mask.sum().item()
+        #     )
+        loss = loss + self.lambda_hinge * loss_mean
+
+        return loss
+    
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        metrics.log_scalar("scale", logging_outputs[0].get("scale"), round=3)
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
+        )
+        if "valid" in split or "test" in split:
+            acc_sum = sum(
+                sum(log.get("prob").argmax(dim=-1) == log.get("target"))
+                for log in logging_outputs
+            )
+            
+            prob_list = []
+            if len(logging_outputs) == 1:
+                prob_list.append(logging_outputs[0].get("prob"))
+            else:
+                for i in range(len(logging_outputs)-1):
+                    prob_list.append(logging_outputs[i].get("prob"))
+            probs = torch.cat(prob_list, dim=0)
+            
+            metrics.log_scalar(
+                f"{split}_acc", acc_sum / sample_size, sample_size, round=3
+            )
+
+            metrics.log_scalar(
+                "valid_neg_loss", -loss_sum / sample_size / math.log(2), sample_size, round=3
+            )
+            
+            
+            targets = torch.cat(
+                [log.get("target", 0) for log in logging_outputs], dim=0
+            )
+            print(targets.shape, probs.shape)
+
+            targets = targets[:len(probs)]
+            bedroc_list = []
+            auc_list = []
+            for i in range(len(probs)):
+                prob = probs[i]
+                target = targets[i]
+                label = torch.zeros_like(prob)
+                label[target]=1.0
+                cur_auc = roc_auc_score(label.cpu(), prob.cpu())
+                auc_list.append(cur_auc)
+                bedroc = calculate_bedroc(label.cpu(), prob.cpu(), 80.5)
+                bedroc_list.append(bedroc)
+            bedroc = np.mean(bedroc_list)
+            auc = np.mean(auc_list)
+            
+            top_k_acc = top_k_accuracy_score(targets.cpu(), probs.cpu(), k=3, normalize=True)
+            metrics.log_scalar(f"{split}_auc", auc, sample_size, round=3)
+            metrics.log_scalar(f"{split}_bedroc", bedroc, sample_size, round=3)
+            metrics.log_scalar(f"{split}_top3_acc", top_k_acc, sample_size, round=3)
+
+            
+
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return is_train
+
+
+
+@register_loss("in_batch_softmax_weighted")
+class IBSLoss_weighted(CrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+        self.lambda_property  = 0.05
+
+    def forward(self, model, sample, reduce=True, fix_encoder=False):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        net_output = model(
+            **sample["net_input"],
+            smi_list = sample["smi_name"],
+            pocket_list = sample["pocket_name"],
+            features_only=True,
+            fix_encoder=fix_encoder,
+            is_train = self.training
+        )
+        
+        logit_output = net_output[0]
+        loss_diff,_,_ = net_output[2]
+        loss = self.compute_loss(model, logit_output, sample, reduce=reduce)
+        #print(loss.item(), loss_diff.item())  15:1.9
+        loss = loss + 0.5 * loss_diff
+        sample_size = logit_output.size(0)
+        targets = torch.arange(sample_size, dtype=torch.long).cuda()
+        affinities = sample["target"]["finetune_target"].view(-1)
+        if not self.training:
+            logit_output = logit_output[:,:sample_size]
+            probs = F.softmax(logit_output.float(), dim=-1).view(
+                -1, logit_output.size(-1)
+            )
+            logging_output = {
+                "loss": loss.data,
+                "prob": probs.data,
+                "target": targets,
+                "smi_name": sample["smi_name"],
+                "sample_size": sample_size,
+                "bsz": targets.size(0),
+                "scale": net_output[1].data,
+                "affinity": affinities,
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "sample_size": sample_size,
+                "bsz": targets.size(0),
+                "scale": net_output[1].data
+            }
+        return loss, sample_size, logging_output
+
+    def compute_loss(self, model, net_output, sample, reduce=True):
+        #=============
+        # weighted_loss
+        logits = net_output.float()
+        B = logits.size(0)
+        logits = self._apply_property_weight(logits, sample["scaffold_fp"], sample["decoy_scaffold_fp"])
+        #=============
+        lprobs_pocket = F.log_softmax(logits, dim=-1)
+        lprobs_pocket = lprobs_pocket.view(-1, lprobs_pocket.size(-1))
+        sample_size = lprobs_pocket.size(0)
+        targets= torch.arange(sample_size, dtype=torch.long).view(-1).cuda()
+
+        # pocket retrieve mol
+        loss_pocket = F.nll_loss(
+            lprobs_pocket,
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        
+        logits_mol_side = logits[:, :B] 
+        lprobs_mol = F.log_softmax(torch.transpose(logits_mol_side, 0, 1), dim=-1)
+        lprobs_mol = lprobs_mol.view(-1, lprobs_mol.size(-1))
+        lprobs_mol = lprobs_mol[:sample_size]
+
+        # mol retrieve pocket
+        loss_mol = F.nll_loss(
+            lprobs_mol,
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        
+        loss = 0.5 * loss_pocket + 0.5 * loss_mol
+        return loss
+    
+    def _apply_property_weight(self, logits, prop_vec_batch, decoy_prop_vec_batch):
+        if self.lambda_property == 0: 
+            return logits
+
+        B, L = logits.shape
+        X_pos = prop_vec_batch.float().to(logits.device)
+        X_dec = decoy_prop_vec_batch.float().to(logits.device)
+        X_all = torch.cat([X_pos, X_dec], dim=0)
+
+        S = X_pos @ X_all.t()    # [B, L]
+        idx = torch.arange(B, device=logits.device)
+        S[idx, idx] = 0.0 
+        # mask_high = S > 0.9   # maybe false negative
+        # S[mask_high] = 0.0
+        alpha = 1.0 + self.lambda_property * S
+        logits = logits + torch.log(alpha + 1e-8)
+
+        return logits
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        metrics.log_scalar("scale", logging_outputs[0].get("scale"), round=3)
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
+        )
+        if "valid" in split or "test" in split:
+            acc_sum = sum(
+                sum(log.get("prob").argmax(dim=-1) == log.get("target"))
+                for log in logging_outputs
+            )
+            
+            prob_list = []
+            if len(logging_outputs) == 1:
+                prob_list.append(logging_outputs[0].get("prob"))
+            else:
+                for i in range(len(logging_outputs)-1):
+                    prob_list.append(logging_outputs[i].get("prob"))
+            probs = torch.cat(prob_list, dim=0)
+            
+            metrics.log_scalar(
+                f"{split}_acc", acc_sum / sample_size, sample_size, round=3
+            )
+
+            metrics.log_scalar(
+                "valid_neg_loss", -loss_sum / sample_size / math.log(2), sample_size, round=3
+            )
+            
+            
+            targets = torch.cat(
+                [log.get("target", 0) for log in logging_outputs], dim=0
+            )
+            print(targets.shape, probs.shape)
+
+            targets = targets[:len(probs)]
+            bedroc_list = []
+            auc_list = []
+            for i in range(len(probs)):
+                prob = probs[i]
+                target = targets[i]
+                label = torch.zeros_like(prob)
+                label[target]=1.0
+                cur_auc = roc_auc_score(label.cpu(), prob.cpu())
+                auc_list.append(cur_auc)
+                bedroc = calculate_bedroc(label.cpu(), prob.cpu(), 80.5)
+                bedroc_list.append(bedroc)
+            bedroc = np.mean(bedroc_list)
+            auc = np.mean(auc_list)
+            
+            top_k_acc = top_k_accuracy_score(targets.cpu(), probs.cpu(), k=3, normalize=True)
+            metrics.log_scalar(f"{split}_auc", auc, sample_size, round=3)
+            metrics.log_scalar(f"{split}_bedroc", bedroc, sample_size, round=3)
+            metrics.log_scalar(f"{split}_top3_acc", top_k_acc, sample_size, round=3)
+
+            
+
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return is_train
+
+
+
+
+
+
+@register_loss("multi_task_BCE")
+class MultiTaskBCELoss(CrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+
+    def forward(self, model, sample, reduce=True, fix_encoder=False):
+        """Compute the loss for the given sample.
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        net_output = model(
+            **sample["net_input"],
+            masked_tokens=None,
+            features_only=True,
+            classification_head_name=self.args.classification_head_name,
+            fix_encoder=fix_encoder
+        )
+        logit_output = net_output[0]
+        is_valid = sample["target"]["finetune_target"] > -0.5
+        loss = self.compute_loss(
+            model, logit_output, sample, reduce=reduce, is_valid=is_valid
+        )
+        sample_size = sample["target"]["finetune_target"].size(0)
+        if not self.training:
+            probs = torch.sigmoid(logit_output.float()).view(-1, logit_output.size(-1))
+            logging_output = {
+                "loss": loss.data,
+                "prob": probs.data,
+                "target": sample["target"]["finetune_target"].view(-1).data,
+                "num_task": self.args.num_classes,
+                "sample_size": sample_size,
+                "conf_size": self.args.conf_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        return loss, sample_size, logging_output
+
+    def compute_loss(self, model, net_output, sample, reduce=True, is_valid=None):
+        pred = net_output[is_valid].float()
+        targets = sample["target"]["finetune_target"][is_valid].float()
+        loss = F.binary_cross_entropy_with_logits(
+            pred,
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        return loss
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
+        )
+        if "valid" in split or "test" in split:
+            agg_auc_list = []
+            num_task = logging_outputs[0].get("num_task", 0)
+            conf_size = logging_outputs[0].get("conf_size", 0)
+            y_true = (
+                torch.cat([log.get("target", 0) for log in logging_outputs], dim=0)
+                .view(-1, conf_size, num_task)
+                .cpu()
+                .numpy()
+                .mean(axis=1)
+            )
+            y_pred = (
+                torch.cat([log.get("prob") for log in logging_outputs], dim=0)
+                .view(-1, conf_size, num_task)
+                .cpu()
+                .numpy()
+                .mean(axis=1)
+            )
+            # [test_size, num_classes] = [test_size * conf_size, num_classes].mean(axis=1)
+            for i in range(y_true.shape[1]):
+                # AUC is only defined when there is at least one positive data.
+                if np.sum(y_true[:, i] == 1) > 0 and np.sum(y_true[:, i] == 0) > 0:
+                    # ignore nan values
+                    is_labeled = y_true[:, i] > -0.5
+                    agg_auc_list.append(
+                        roc_auc_score(y_true[is_labeled, i], y_pred[is_labeled, i])
+                    )
+            if len(agg_auc_list) < y_true.shape[1]:
+                warnings.warn("Some target is missing!")
+            if len(agg_auc_list) == 0:
+                raise RuntimeError(
+                    "No positively labeled data available. Cannot compute Average Precision."
+                )
+            agg_auc = sum(agg_auc_list) / len(agg_auc_list)
+            metrics.log_scalar(f"{split}_agg_auc", agg_auc, sample_size, round=4)
+
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return is_train
+
+@register_loss("BCE")
+class BCELoss(CrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+
+    def forward(self, model, sample, reduce=True, fix_encoder=False):
+        """Compute the loss for the given sample.
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        net_output = model(
+            **sample["net_input"],
+            smi_list = sample["smi_name"],
+            pocket_list = sample["pocket_name"],
+            features_only=True,
+            fix_encoder=fix_encoder
+        )
+        logit_output = net_output
+        loss = self.compute_loss(
+            model, logit_output, sample, reduce=reduce
+        )
+        sample_size = sample["target"]["finetune_target"].size(0)
+
+        if not self.training:
+            probs = torch.sigmoid(logit_output.float())
+            #print(probs.size())
+            logging_output = {
+                "loss": loss.data,
+                "prob": probs.data,
+                "target": sample["target"]["finetune_target"].view(-1).data,
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        return loss, sample_size, logging_output
+
+    def compute_loss(self, model, net_output, sample, reduce=True, is_valid=None):
+        pred = net_output.float()
+        targets = sample["target"]["finetune_target"].float()
+        loss = F.binary_cross_entropy_with_logits(
+            pred,
+            targets,
+            reduction="sum" if reduce else "none",
+        )
+        return loss
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
+        )
+        if "valid" in split or "test" in split:
+            y_true_list = []
+            y_pred_list = []
+            y_true_list = [log.get("target", 0) for log in logging_outputs]
+            y_pred_list = [log.get("prob") for log in logging_outputs]
+            y_true = (
+                torch.cat(y_true_list, dim=0)
+                .cpu()
+                .numpy()
+            )
+            y_pred = (
+                torch.cat(y_pred_list, dim=0)
+                .cpu()
+                .numpy()
+            )
+            # [test_size, num_classes] = [test_size * conf_size, num_classes].mean(axis=1)
+
+            auc = roc_auc_score(y_true , y_pred)
+                    
+
+            agg_auc = auc
+            metrics.log_scalar(f"{split}_agg_auc", agg_auc, sample_size, round=4)
+
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return is_train
+
+@register_loss("finetune_cross_entropy_pocket")
+class FinetuneCrossEntropyPocketLoss(FinetuneCrossEntropyLoss):
+    def __init__(self, task):
+        super().__init__(task)
+
+    def forward(self, model, sample, reduce=True):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        net_output = model(
+            **sample["net_input"],
+            features_only=True,
+            classification_head_name=self.args.classification_head_name,
+        )
+        logit_output = net_output[0]
+        loss = self.compute_loss(model, logit_output, sample, reduce=reduce)
+        sample_size = sample["target"]["finetune_target"].size(0)
+        if not self.training:
+            probs = F.softmax(logit_output.float(), dim=-1).view(
+                -1, logit_output.size(-1)
+            )
+            logging_output = {
+                "loss": loss.data,
+                "prob": probs.data,
+                "target": sample["target"]["finetune_target"].view(-1).data,
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        else:
+            logging_output = {
+                "loss": loss.data,
+                "sample_size": sample_size,
+                "bsz": sample["target"]["finetune_target"].size(0),
+            }
+        return loss, sample_size, logging_output
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        # we divide by log(2) to convert the loss from base e to base 2
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
+        )
+        if "valid" in split or "test" in split:
+            acc_sum = sum(
+                sum(log.get("prob").argmax(dim=-1) == log.get("target"))
+                for log in logging_outputs
+            )
+            metrics.log_scalar(
+                f"{split}_acc", acc_sum / sample_size, sample_size, round=3
+            )
+            preds = (
+                torch.cat(
+                    [log.get("prob").argmax(dim=-1) for log in logging_outputs], dim=0
+                )
+                .cpu()
+                .numpy()
+            )
+            targets = (
+                torch.cat([log.get("target", 0) for log in logging_outputs], dim=0)
+                .cpu()
+                .numpy()
+            )
+            metrics.log_scalar(f"{split}_pre", precision_score(targets, preds), round=3)
+            metrics.log_scalar(f"{split}_rec", recall_score(targets, preds), round=3)
+            metrics.log_scalar(
+                f"{split}_f1", f1_score(targets, preds), sample_size, round=3
+            )

data/unimol/losses/unimol.py

@@ -0,0 +1,210 @@
+# Copyright (c) DP Technology.
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn.functional as F
+from unicore import metrics
+from unicore.losses import UnicoreLoss, register_loss
+
+
+@register_loss("unimol")
+class UniMolLoss(UnicoreLoss):
+    def __init__(self, task):
+        super().__init__(task)
+        self.padding_idx = task.dictionary.pad()
+        self.seed = task.seed
+        self.dist_mean = 6.312581655060595
+        self.dist_std = 3.3899264663911888
+
+    def forward(self, model, sample, reduce=True):
+        input_key = "net_input"
+        target_key = "target"
+        masked_tokens = sample[target_key]["tokens_target"].ne(self.padding_idx)
+        sample_size = masked_tokens.long().sum()
+        (
+            logits_encoder,
+            encoder_distance,
+            encoder_coord,
+            x_norm,
+            delta_encoder_pair_rep_norm,
+        ) = model(**sample[input_key], encoder_masked_tokens=masked_tokens)
+        target = sample[target_key]["tokens_target"]
+        if masked_tokens is not None:
+            target = target[masked_tokens]
+        masked_token_loss = F.nll_loss(
+            F.log_softmax(logits_encoder, dim=-1, dtype=torch.float32),
+            target,
+            ignore_index=self.padding_idx,
+            reduction="mean",
+        )
+        masked_pred = logits_encoder.argmax(dim=-1)
+        masked_hit = (masked_pred == target).long().sum()
+        masked_cnt = sample_size
+        loss = masked_token_loss * self.args.masked_token_loss
+        logging_output = {
+            "sample_size": 1,
+            "bsz": sample[target_key]["tokens_target"].size(0),
+            "seq_len": sample[target_key]["tokens_target"].size(1)
+            * sample[target_key]["tokens_target"].size(0),
+            "masked_token_loss": masked_token_loss.data,
+            "masked_token_hit": masked_hit.data,
+            "masked_token_cnt": masked_cnt,
+        }
+
+        if encoder_coord is not None:
+            # real = mask + delta
+            coord_target = sample[target_key]["coord_target"]
+            masked_coord_loss = F.smooth_l1_loss(
+                encoder_coord[masked_tokens].view(-1, 3).float(),
+                coord_target[masked_tokens].view(-1, 3),
+                reduction="mean",
+                beta=1.0,
+            )
+            loss = loss + masked_coord_loss * self.args.masked_coord_loss
+            # restore the scale of loss for displaying
+            logging_output["masked_coord_loss"] = masked_coord_loss.data
+
+        if encoder_distance is not None:
+            dist_masked_tokens = masked_tokens
+            masked_dist_loss = self.cal_dist_loss(
+                sample, encoder_distance, dist_masked_tokens, target_key, normalize=True
+            )
+            loss = loss + masked_dist_loss * self.args.masked_dist_loss
+            logging_output["masked_dist_loss"] = masked_dist_loss.data
+
+        if self.args.x_norm_loss > 0 and x_norm is not None:
+            loss = loss + self.args.x_norm_loss * x_norm
+            logging_output["x_norm_loss"] = x_norm.data
+
+        if (
+            self.args.delta_pair_repr_norm_loss > 0
+            and delta_encoder_pair_rep_norm is not None
+        ):
+            loss = (
+                loss + self.args.delta_pair_repr_norm_loss * delta_encoder_pair_rep_norm
+            )
+            logging_output[
+                "delta_pair_repr_norm_loss"
+            ] = delta_encoder_pair_rep_norm.data
+
+        logging_output["loss"] = loss.data
+        return loss, 1, logging_output
+
+    @staticmethod
+    def reduce_metrics(logging_outputs, split="valid") -> None:
+        """Aggregate logging outputs from data parallel training."""
+        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
+        bsz = sum(log.get("bsz", 0) for log in logging_outputs)
+        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
+        seq_len = sum(log.get("seq_len", 0) for log in logging_outputs)
+        metrics.log_scalar("loss", loss_sum / sample_size, sample_size, round=3)
+        metrics.log_scalar("seq_len", seq_len / bsz, 1, round=3)
+
+        masked_loss = sum(log.get("masked_token_loss", 0) for log in logging_outputs)
+        metrics.log_scalar(
+            "masked_token_loss", masked_loss / sample_size, sample_size, round=3
+        )
+
+        masked_acc = sum(
+            log.get("masked_token_hit", 0) for log in logging_outputs
+        ) / sum(log.get("masked_token_cnt", 0) for log in logging_outputs)
+        metrics.log_scalar("masked_acc", masked_acc, sample_size, round=3)
+
+        masked_coord_loss = sum(
+            log.get("masked_coord_loss", 0) for log in logging_outputs
+        )
+        if masked_coord_loss > 0:
+            metrics.log_scalar(
+                "masked_coord_loss",
+                masked_coord_loss / sample_size,
+                sample_size,
+                round=3,
+            )
+
+        masked_dist_loss = sum(
+            log.get("masked_dist_loss", 0) for log in logging_outputs
+        )
+        if masked_dist_loss > 0:
+            metrics.log_scalar(
+                "masked_dist_loss", masked_dist_loss / sample_size, sample_size, round=3
+            )
+
+        x_norm_loss = sum(log.get("x_norm_loss", 0) for log in logging_outputs)
+        if x_norm_loss > 0:
+            metrics.log_scalar(
+                "x_norm_loss", x_norm_loss / sample_size, sample_size, round=3
+            )
+
+        delta_pair_repr_norm_loss = sum(
+            log.get("delta_pair_repr_norm_loss", 0) for log in logging_outputs
+        )
+        if delta_pair_repr_norm_loss > 0:
+            metrics.log_scalar(
+                "delta_pair_repr_norm_loss",
+                delta_pair_repr_norm_loss / sample_size,
+                sample_size,
+                round=3,
+            )
+
+    @staticmethod
+    def logging_outputs_can_be_summed(is_train) -> bool:
+        """
+        Whether the logging outputs returned by `forward` can be summed
+        across workers prior to calling `reduce_metrics`. Setting this
+        to True will improves distributed training speed.
+        """
+        return True
+
+    def cal_dist_loss(self, sample, dist, masked_tokens, target_key, normalize=False):
+        dist_masked_tokens = masked_tokens
+        masked_distance = dist[dist_masked_tokens, :]
+        masked_distance_target = sample[target_key]["distance_target"][
+            dist_masked_tokens
+        ]
+        non_pad_pos = masked_distance_target > 0
+        if normalize:
+            masked_distance_target = (
+                masked_distance_target.float() - self.dist_mean
+            ) / self.dist_std
+        masked_dist_loss = F.smooth_l1_loss(
+            masked_distance[non_pad_pos].view(-1).float(),
+            masked_distance_target[non_pad_pos].view(-1),
+            reduction="mean",
+            beta=1.0,
+        )
+        return masked_dist_loss
+
+
+@register_loss("unimol_infer")
+class UniMolInferLoss(UnicoreLoss):
+    def __init__(self, task):
+        super().__init__(task)
+        self.padding_idx = task.dictionary.pad()
+
+    def forward(self, model, sample, reduce=True):
+        """Compute the loss for the given sample.
+
+        Returns a tuple with three elements:
+        1) the loss
+        2) the sample size, which is used as the denominator for the gradient
+        3) logging outputs to display while training
+        """
+        input_key = "net_input"
+        target_key = "target"
+        src_tokens = sample[input_key]["src_tokens"].ne(self.padding_idx)
+        (
+            encoder_rep,
+            encoder_pair_rep,
+        ) = model(**sample[input_key], features_only=True)
+        sample_size = sample[input_key]["src_tokens"].size(0)
+        encoder_pair_rep_list = []
+        for i in range(sample_size):  # rm padding token
+            encoder_pair_rep_list.append(encoder_pair_rep[i][src_tokens[i], :][:, src_tokens[i]].data.cpu().numpy())
+        logging_output = {
+                "mol_repr_cls": encoder_rep[:, 0, :].data.cpu().numpy(),  # get cls token
+                "pair_repr": encoder_pair_rep_list,
+                "smi_name": sample[target_key]["smi_name"],
+                "bsz": sample[input_key]["src_tokens"].size(0),
+            }
+        return 0, sample_size, logging_output

data/unimol/models/__init__.py

@@ -0,0 +1,3 @@
+from .drugclip import BindingAffinityModel
+from .transformer_encoder_with_pair import TransformerEncoderWithPair
+from .unimol import UniMolModel