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from pathlib import Path
import re
from typing import Dict, List, Optional, Union
import numpy as np
import pytorch_lightning as pl
import torch
from torch import Tensor
from torch.utils.data import DataLoader
from rdkit.Chem import Mol
from boltzgen.data import const
from boltzgen.data.data import Input
from boltzgen.data.feature.featurizer import Featurizer
from boltzgen.data.pad import pad_to_max
from boltzgen.data.mol import load_canonicals, load_molecules
from boltzgen.data.parse.schema import YamlDesignParser
from boltzgen.data.template.features import load_dummy_templates
from boltzgen.data.tokenize.tokenizer import Tokenizer
from boltzgen.data.data import Input
from boltzgen.data.select.protein import ProteinSelector
@dataclass
class DataConfig:
"""Data configuration."""
moldir: str
multiplicity: int
yaml_path: Union[List[str], str]
tokenizer: Tokenizer
featurizer: Featurizer
backbone_only: bool = False
atom14: bool = False
atom37: bool = False
design: bool = True
compute_affinity: bool = False
disulfide_prob: float = 1.0
disulfide_on: bool = False
skip_existing: bool = False
skip_offset: int = 0
diffusion_samples: int = 1
output_dir: Optional[str] = None
@dataclass
class Dataset:
yaml_path: Union[List[str], str]
tokenizer: Tokenizer
featurizer: Featurizer
multiplicity: int = 1
def collate(data: List[Dict[str, Tensor]]) -> Dict[str, Tensor]:
"""Collate the data.
Parameters
----------
data : List[Dict[str, Tensor]]
The data to collate.
Returns
-------
Dict[str, Tensor]
The collated data.
"""
# Get the keys
keys = data[0].keys()
# Collate the data
collated = {}
for key in keys:
values = [d[key] for d in data]
if key not in [
"all_coords",
"all_resolved_mask",
"crop_to_all_atom_map",
"chain_symmetries",
"amino_acids_symmetries",
"ligand_symmetries",
"activity_name",
"activity_qualifier",
"sid",
"cid",
"aid",
"normalized_protein_accession",
"pair_id",
"record",
"id",
"structure",
"tokenized",
"structure_bonds",
"extra_mols",
"data_sample_idx",
]:
# Check if all have the same shape
shape = values[0].shape
if not all(v.shape == shape for v in values):
values = pad_to_max(values, 0)
else:
values = torch.stack(values, dim=0)
# Stack the values
collated[key] = values
return collated
class PredictionDataset(torch.utils.data.Dataset):
"""Base iterable dataset."""
def __init__(
self,
dataset: Dataset,
canonicals: dict[str, Mol],
moldir: str,
backbone_only: bool = False,
atom14: bool = False,
atom37: bool = False,
extra_features: Optional[List[str]] = None,
design: bool = True,
compute_affinity: bool = False,
disulfide_prob: float = 1.0,
disulfide_on: bool = False,
skip_offset: int = 0,
) -> None:
"""Initialize the training dataset.
Parameters
----------
datasets : List[Dataset]
The datasets to sample from.
"""
super().__init__()
self.dataset = dataset
self.moldir = moldir
self.canonicals = canonicals
self.backbone_only = backbone_only
self.atom14 = atom14
self.atom37 = atom37
self.skip_offset = skip_offset
path = dataset.yaml_path
self.yaml_paths = [path] if isinstance(path, str) else path
for path in self.yaml_paths:
filename = Path(path).name
if re.search(r"_\d+\.yaml$", filename):
raise ValueError(
f"Illegal YAML filename for '{str(path)}': names must not end with the pattern _\\d+\\.yaml so, e.g., the ends '_001.yaml' or '_4.yaml' are not allowed."
"This pattern is reserved for internal file indexing. Sorry :)"
)
if "_native" in filename:
raise ValueError(
f"Illegal YAML filename for '{str(path)}': names must not contain '_native' because this substring is reserved for native structure companions. Sorry :)"
)
self.extra_features = (
set(extra_features) if extra_features is not None else set()
)
self.selector = (
ProteinSelector(
design_neighborhood_sizes=[2, 4, 6, 8, 10, 12, 14, 16, 18],
substructure_neighborhood_sizes=[2, 4, 6, 8, 10, 12, 24],
structure_condition_prob=1.0,
distance_noise_std=1,
run_selection=True,
specify_binding_sites=True,
ss_condition_prob=0.1,
select_all=False,
chain_reindexing=False,
)
)
self.design = design
self.compute_affinity = compute_affinity
self.disulfide_prob = disulfide_prob
self.disulfide_on = disulfide_on
self.mols = {}
self.parser = YamlDesignParser(mol_dir=self.moldir)
def __getitem__(self, idx: int) -> Dict:
"""Get an item from the dataset.
Returns
-------
Dict[str, Tensor]
The sampled data features.
"""
path = Path(self.yaml_paths[idx % len(self.yaml_paths)])
feat = self.get_sample(path)
data_sample_idx = idx // len(self.yaml_paths) + self.skip_offset
if self.dataset.multiplicity > 1:
feat["data_sample_idx"] = data_sample_idx
return feat
def get_sample(self, path: Path, sample_id: Optional[str] = None) -> Dict:
# Get itemn also needs to take a smaple id as input
parsed = self.parser.parse_yaml(
path, mol_dir=self.moldir, mols=self.mols
)
structure = parsed.structure
design_info = parsed.design_info
# Tokenize structure
tokenized = self.dataset.tokenizer.tokenize(structure)
# Transfer conditioning information that is stored in tokens
token_to_res = tokenized.token_to_res
tokenized.tokens["design_mask"] = design_info.res_design_mask[token_to_res]
tokenized.tokens["binding_type"] = design_info.res_binding_type[token_to_res]
tokenized.tokens["structure_group"] = design_info.res_structure_groups[
token_to_res
]
# Transfer per-residue amino acid constraints (shape: num_tokens x 20)
tokenized.tokens["aa_constraint_mask"] = design_info.res_aa_constraint_mask[
token_to_res
]
# Propagate design mask to obtain chain_design_mask (True whenever something is covalently bound to any residue that is in a chain that contains a design residue).
chain_design_mask = tokenized.tokens["design_mask"].astype(bool)
asym_id = tokenized.tokens["asym_id"]
while True:
design_chains = np.unique(asym_id[chain_design_mask])
chain_propagated = np.isin(asym_id, design_chains)
for i, j, _ in tokenized.bonds:
if any([chain_propagated[i], chain_propagated[j]]):
chain_propagated[i] = True
chain_propagated[j] = True
if np.equal(chain_propagated, chain_design_mask).all():
break
chain_design_mask = chain_propagated.astype(bool)
# Try to find molecules in the dataset moldir if provided
# Find missing ones in global moldir and check if all found
molecules = {}
molecules.update(self.canonicals)
mol_names = set(tokenized.tokens["res_name"].tolist())
mol_names = mol_names - set(self.canonicals.keys())
mol_names = mol_names - set(parsed.extra_mols.keys())
if self.moldir is not None:
molecules.update(load_molecules(self.moldir, mol_names))
mol_names = mol_names - set(molecules.keys())
molecules.update(load_molecules(self.moldir, mol_names))
molecules.update(parsed.extra_mols)
# Finalize input data
input_data = Input(
tokens=tokenized.tokens,
bonds=tokenized.bonds,
token_to_res=token_to_res,
structure=structure,
msa={},
templates=None,
)
# Compute features
features = self.dataset.featurizer.process(
input_data,
molecules=molecules,
random=np.random.default_rng(None),
training=False,
max_seqs=1,
backbone_only=self.backbone_only,
atom14=self.atom14,
atom37=self.atom37,
design=self.design,
override_method="X-RAY DIFFRACTION",
compute_affinity=self.compute_affinity,
disulfide_prob=self.disulfide_prob,
disulfide_on=self.disulfide_on,
)
# transfer secondary structure conditioning
ss_type = design_info.res_ss_types[token_to_res]
features["ss_type"] = torch.from_numpy(ss_type).to(features["ss_type"])
features["design_ss_mask"][ss_type != const.ss_type_ids["UNSPECIFIED"]] = 1
# set chain_design_mask
features["chain_design_mask"] = torch.from_numpy(chain_design_mask)
# Compute template features
templates_features = load_dummy_templates(
tdim=1, num_tokens=len(features["res_type"])
)
features.update(templates_features)
# set last necessary features
features["idx_dataset"] = torch.tensor(1)
# If a smaple id is provided then this should be the sample id instead of path.stem
if sample_id is not None:
features["id"] = sample_id
else:
features["id"] = path.stem
if "structure" in self.extra_features:
features["structure"] = structure
if "tokenized" in self.extra_features:
features["tokenized"] = tokenized
return features
def __len__(self) -> int:
"""Get the length of the dataset.
Returns
-------
int
The length of the dataset.
"""
total = len(self.yaml_paths) * (self.dataset.multiplicity - self.skip_offset)
return max(total, 0)
class FromYamlDataModule(pl.LightningDataModule):
"""DataModule for BoltzGen."""
def __init__(
self, cfg: DataConfig, batch_size, num_workers, pin_memory, extra_features=None
) -> None:
"""Initialize the DataModule.
Parameters
----------
config : DataConfig
The data configuration.
"""
super().__init__()
if cfg.skip_existing and cfg.output_dir is not None:
design_dir = Path(cfg.output_dir)
max_idx: int = -1
if design_dir.exists():
pattern = re.compile(r"_(\d+)(?:\.[^.]+)$")
max_idx = max(
(
int(m.group(1))
for fp in design_dir.iterdir()
if fp.suffix in {".cif", ".pdb"}
and not any(s in fp.name for s in ("_native.cif", "_metadata.npz"))
for m in [pattern.search(fp.name)]
if m
),
default=-1,
)
n_samples = getattr(cfg, "diffusion_samples", 1)
cfg.skip_offset = (max_idx // max(n_samples, 1)) + 1 if max_idx >= 0 else 0
self.cfg = cfg
self.batch_size = batch_size
self.num_workers = num_workers
self.pin_memory = pin_memory
self.collate = collate
dataset = Dataset(
yaml_path=cfg.yaml_path,
multiplicity=cfg.multiplicity,
tokenizer=cfg.tokenizer,
featurizer=cfg.featurizer,
)
# Load canonical molecules
canonicals = load_canonicals(cfg.moldir)
self.predict_set = PredictionDataset(
dataset=dataset,
canonicals=canonicals,
moldir=Path(cfg.moldir),
backbone_only=cfg.backbone_only,
atom14=cfg.atom14,
atom37=cfg.atom37,
extra_features=extra_features,
design=cfg.design,
compute_affinity=cfg.compute_affinity,
disulfide_prob=cfg.disulfide_prob,
disulfide_on=cfg.disulfide_on,
skip_offset=cfg.skip_offset,
)
def predict_dataloader(self) -> DataLoader:
"""Get the training dataloader.
Returns
-------
DataLoader
The training dataloader.
"""
return DataLoader(
self.predict_set,
batch_size=self.batch_size,
num_workers=self.num_workers,
pin_memory=self.pin_memory,
shuffle=False,
collate_fn=collate,
)
def transfer_batch_to_device(
self,
batch: Dict,
device: torch.device,
dataloader_idx: int = 0, # noqa: ARG002
) -> Dict:
"""Transfer a batch to the given device.
Parameters
----------
batch : Dict
The batch to transfer.
device : torch.device
The device to transfer to.
Returns
-------
np.Any
The transferred batch.
"""
for key in batch:
if key not in [
"all_coords",
"all_resolved_mask",
"crop_to_all_atom_map",
"chain_symmetries",
"amino_acids_symmetries",
"ligand_symmetries",
"activity_name",
"activity_qualifier",
"sid",
"cid",
"normalized_protein_accession",
"pair_id",
"record",
"id",
"structure",
"tokenized",
"structure_bonds",
"extra_mols",
"data_sample_idx",
]:
batch[key] = batch[key].to(device)
return batch
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