File size: 2,445 Bytes
4947683 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | #!/usr/bin/python
# -*- coding:utf-8 -*-
import os
import argparse
from typing import List, Tuple, Optional
import numpy as np
import pandas as pd
from rdkit import Chem
import json
import pickle
from utils.logger import print_log
from data.format import Block, Atom
from data.format import VOCAB as VOCAB
from data.converter.df_to_blocks import df_to_blocks
from data.converter.blocks_to_data import blocks_to_data
from data.mmap_dataset import create_mmap
from data.atom3d_lmdb import LMDBDataset
from data.tokenizer.tokenize_3d import TOKENIZER, tokenize_3d
def parse():
parser = argparse.ArgumentParser(description='Process molecule data from LBA dataset.')
parser.add_argument('--base_path', type=str, required=True,
help='Directory of rdkit_folder')
parser.add_argument('--out_dir', type=str, required=True,
help='Output directory')
return parser.parse_args()
def process_iterator(base_data):
for item in base_data:
# receptor
blocks1 = df_to_blocks(item['atoms_pocket'], key_atom_name='name')
# ligand (each block is an atom)
blocks2 = []
for row in item['atoms_ligand'].itertuples():
atom = Atom(
atom_name=getattr(row, 'name'), # e.g. C1, C2, ..., these position code will be a unified encoding such as <sm> (small molecule) in our framework
coordinate=[getattr(row, axis) for axis in ['x', 'y', 'z']],
element=getattr(row, 'element'),
pos_code=VOCAB.atom_pos_sm
)
blocks2.append(Block(
symbol=atom.element.lower(),
units=[atom]
))
data = blocks_to_data(blocks1, blocks2)
for key in data:
if isinstance(data[key], np.ndarray):
data[key] = data[key].tolist()
# id, data, [len] (only save the lengths as the properties)
yield item['id'], data, [item['scores']['neglog_aff']]
def main(args):
for split in ['train', 'val', 'test']:
print_log(f'Processing {split} dataset ...')
base_data = LMDBDataset(os.path.join(args.base_path, split))
create_mmap(
process_iterator(base_data),
os.path.join(args.out_dir,split),
len(base_data)
)
print_log('Finished!')
if __name__ == '__main__':
main(parse()) |