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from pathlib import Path
from typing import Dict, List
import numpy as np
import torch
from pytorch_lightning import LightningModule, Trainer
from pytorch_lightning.callbacks import BasePredictionWriter
from torch import Tensor
from tqdm import tqdm
from boltzgen.data import const
from boltzgen.data.data import (
Structure,
convert_ccd,
)
from boltzgen.data.feature.featurizer import (
res_from_atom14,
res_from_atom37,
res_all_gly,
)
from boltzgen.data.write.mmcif import to_mmcif
from boltzgen.data.write.pdb import to_pdb
from boltzgen.model.loss.diffusion import weighted_rigid_align
from boltzgen.model.modules.masker import BoltzMasker
class FoldingWriter(BasePredictionWriter):
"""Custom writer for predictions."""
def __init__(self, design_dir: str, designfolding: bool = False) -> None:
super().__init__(write_interval="batch")
self.designfolding = designfolding
if design_dir is not None:
self.init_outdir(design_dir)
def init_outdir(self, design_dir):
self.outdir = Path(design_dir) / (
const.folding_design_dirname
if self.designfolding
else const.folding_dirname
)
self.refold_cif_dir = Path(design_dir) / (
const.refold_design_cif_dirname
if self.designfolding
else const.refold_cif_dirname
)
self.refold_cif_dir.mkdir(parents=True, exist_ok=True)
self.outdir.mkdir(exist_ok=True, parents=True)
self.failed = 0
def write_on_batch_end( # noqa: PLR0915
self,
trainer: Trainer = None, # noqa: ARG002
pl_module: LightningModule = None, # noqa: ARG002
prediction: Dict[str, Tensor] = None,
batch_indices: List[int] = None, # noqa: ARG002
batch: Dict[str, Tensor] = None,
batch_idx: int = None, # noqa: ARG002
dataloader_idx: int = 0, # noqa: ARG002
sample_id: str = None,
) -> None:
"""Write the predictions to disk."""
pred_dict = {}
for key, value in prediction.items():
# check object is tensor
if key in const.eval_keys:
pred_dict[key] = value.cpu().numpy()
np.savez_compressed(self.outdir / f"{batch['id'][0]}.npz", **pred_dict)
# Get best sample
confidence = 0.8 * pred_dict["iptm"] + 0.2 * pred_dict["ptm"]
best_idx = np.argmax(confidence)
best_sample_coords = pred_dict["coords"][best_idx]
prediction_out = {}
for k in prediction:
if k == "coords":
prediction_out[k] = torch.from_numpy(best_sample_coords)
else:
prediction_out[k] = prediction[k][0]
# Write structure
structure, _, _ = Structure.from_feat(prediction_out)
plddt_atom = (
prediction_out["atom_to_token"].float() @ prediction_out["plddt"].float()
)
structure.atoms["bfactor"] = (
plddt_atom[prediction_out["atom_pad_mask"].bool()].float().cpu().numpy()
)
cif_text = to_mmcif(structure)
open(self.refold_cif_dir / f"{batch['id'][0]}.cif", "w").write(cif_text)
# Failed prediction handling
if isinstance(prediction["exception"], bool):
if prediction["exception"]:
self.failed += 1
elif isinstance(prediction["exception"], list):
if prediction["exception"][0]:
self.failed += 1
def on_predict_epoch_end(
self,
trainer: Trainer, # noqa: ARG002
pl_module: LightningModule, # noqa: ARG002
) -> None:
print(f"Number of failed structure predictions: {self.failed}") # noqa: T201
class AffinityWriter(BasePredictionWriter):
"""Custom writer for predictions."""
def __init__(
self,
design_dir: str,
) -> None:
super().__init__(write_interval="batch")
if design_dir is not None:
self.init_outdir(design_dir)
def init_outdir(self, design_dir):
self.outdir = Path(design_dir) / const.affinity_dirname
self.outdir.mkdir(exist_ok=True, parents=True)
self.failed = 0
def write_on_batch_end( # noqa: PLR0915
self,
trainer: Trainer = None, # noqa: ARG002
pl_module: LightningModule = None, # noqa: ARG002
prediction: Dict[str, Tensor] = None,
batch_indices: List[int] = None, # noqa: ARG002
batch: Dict[str, Tensor] = None,
batch_idx: int = None, # noqa: ARG002
dataloader_idx: int = 0, # noqa: ARG002
sample_id: str = None,
) -> None:
"""Write the predictions to disk."""
pred_dict = {}
for key, value in prediction.items():
# check object is tensor
if key in const.eval_keys:
pred_dict[key] = value.cpu().numpy()
np.savez_compressed(self.outdir / f"{batch['id'][0]}.npz", **pred_dict)
if isinstance(prediction["exception"], bool):
if prediction["exception"]:
self.failed += 1
elif isinstance(prediction["exception"], list):
if prediction["exception"][0]:
self.failed += 1
def on_predict_epoch_end(
self,
trainer: Trainer, # noqa: ARG002
pl_module: LightningModule, # noqa: ARG002
) -> None:
print(f"Number of failed affinity predictions: {self.failed}") # noqa: T201
class DesignWriter(BasePredictionWriter):
"""Custom writer for predictions."""
def __init__(
self,
output_dir: str,
res_atoms_only: bool,
save_traj: bool = False,
save_x0_traj: bool = False,
atom14: bool = True,
atom37: bool = False,
backbone_only: bool = False,
inverse_fold: bool = False,
file_suffix: str = "",
write_native: bool = True,
design: bool = True,
) -> None:
"""Initialize the writer.
Parameters
----------
output_dir : str
The directory to save the predictions.
"""
super().__init__(write_interval="batch")
self.mol_dir = Path(output_dir) / const.molecules_dirname
self.mol_dir.mkdir(parents=True, exist_ok=True)
self.save_traj = save_traj
self.save_x0_traj = save_x0_traj
self.res_atoms_only = res_atoms_only
self.file_suffix = file_suffix
self.failed = 0
self.write_native = write_native
self.design = design
# Create the output directories
self.atom14 = atom14
self.atom37 = atom37
self.inverse_fold = inverse_fold
self.backbone_only = backbone_only
self.used_stems = set()
self.init_outdir(output_dir)
def init_outdir(self, outdir):
self.outdir = Path(outdir)
self.outdir.mkdir(parents=True, exist_ok=True)
def write_on_batch_end( # noqa: PLR0915
self,
trainer: Trainer = None, # noqa: ARG002
pl_module: LightningModule = None, # noqa: ARG002
prediction: Dict[str, Tensor] = None,
batch_indices: List[int] = None, # noqa: ARG002
batch: Dict[str, Tensor] = None,
batch_idx: int = None, # noqa: ARG002
dataloader_idx: int = 0, # noqa: ARG002
sample_id: str = None,
) -> None:
if prediction["exception"]:
self.failed += 1
return
n_samples, _, _ = prediction["coords"].shape
# TODO: remove this which is only here for temporary backward compatibility
masker = BoltzMasker(mask=True, mask_backbone=False)
feat_masked = masker(batch)
prediction["ref_element"] = feat_masked["ref_element"]
prediction["ref_atom_name_chars"] = feat_masked["ref_atom_name_chars"]
"""Write the predictions to disk."""
# Check for extra molecules
if batch["extra_mols"] is not None:
extra_mols = batch["extra_mols"][0]
for k, v in extra_mols.items():
with open(self.mol_dir / f"{k}.pkl", "wb") as f:
pickle.dump(v, f)
# write samples to disk
for n in range(n_samples):
# get structure for all generated coords
sample, native = {}, {}
for k in set(prediction.keys()) & set(batch.keys()):
if k == "coords":
native[k] = batch[k][0][0].unsqueeze(0)
sample[k] = prediction[k][n]
if k in const.token_features:
sample[k] = prediction[k][0]
native[k] = batch[k][0]
elif k in const.atom_features:
if k == "coords":
native[k] = batch[k][0][0].unsqueeze(0)
sample[k] = prediction[k][n]
else:
native[k] = batch[k][0]
sample[k] = prediction[k][0]
elif k == "exception":
sample[k] = prediction[k]
native[k] = batch[k]
else:
native[k] = batch[k][0]
sample[k] = prediction[k][0]
native[k] = batch[k][0]
if self.atom14:
sample = res_from_atom14(sample)
elif self.atom37:
sample = res_from_atom37(sample)
elif self.backbone_only:
sample = res_all_gly(sample)
design_mask = batch["design_mask"][0].bool()
assert design_mask.sum() == sample["design_mask"].sum()
if self.inverse_fold:
token_ids = torch.argmax(sample["res_type"], dim=-1)
tokens = [const.tokens[i] for i in token_ids]
ccds = [convert_ccd(token) for token in tokens]
ccds = torch.tensor(ccds).to(sample["res_type"])
sample["ccd"][design_mask] = ccds[design_mask]
try:
structure, _, _ = Structure.from_feat(sample)
str_native, _, _ = Structure.from_feat(native)
# write structure to cif
if sample_id is not None:
file_name = f"{sample_id}_{n}{self.file_suffix}"
else:
stem = str(batch["id"][0])
multiplicity = getattr(trainer.datamodule.cfg, "multiplicity", 1)
total_files = multiplicity * n_samples
sample_idx = (
int(batch["data_sample_idx"][0])
if "data_sample_idx" in batch
else 0
)
global_idx = sample_idx * n_samples + n
if total_files > 1:
num_digits = len(str(total_files - 1))
file_name = (
f"{stem}_{global_idx:0{num_digits}d}{self.file_suffix}"
)
else:
file_name = f"{stem}{self.file_suffix}"
native_path = f"{self.outdir}/{file_name}_native.cif"
gen_path = f"{self.outdir}/{file_name}.cif"
# design mask bfactor
design_mask = batch["design_mask"][0].float()
atom_design_mask = (
sample["atom_to_token"].float() @ design_mask.unsqueeze(-1).float()
)
design_mask = native["design_mask"].float()
atom_design_mask = atom_design_mask.squeeze().bool()
bfactor = atom_design_mask * 100
# binding type bfactor
binding_type = batch["binding_type"][0].float()
atom_binding_type = (
sample["atom_to_token"].float() @ binding_type.unsqueeze(-1).float()
)
atom_binding_type = atom_binding_type.squeeze().int()
bfactor[atom_binding_type == const.binding_type_ids["BINDING"]] = 60
bfactor = atom_design_mask[sample["atom_pad_mask"].bool()].float()
str_native.atoms["bfactor"] = bfactor.cpu().numpy()
structure.atoms["bfactor"] = bfactor.cpu().numpy()
# Add dummy (0-coord) design side chains if inverse fold
if self.inverse_fold:
atom_design_mask_no_pad = atom_design_mask[
native["atom_pad_mask"].bool()
]
res_design_mask = np.array(
[
all(
atom_design_mask_no_pad[
res["atom_idx"] : res["atom_idx"] + res["atom_num"]
]
)
for res in structure.residues
]
)
structure = Structure.add_side_chains(
structure, residue_mask=res_design_mask
)
if self.write_native:
open(native_path, "w").write(to_mmcif(str_native))
pred_binding_mask = prediction["binding_type"][0].cpu().bool().numpy()
if self.design:
chain_design_mask = (
prediction["chain_design_mask"][0].cpu().bool().numpy()
)
pred_design_mask = prediction["design_mask"][0].cpu().bool().numpy()
design_color_features = np.ones_like(pred_binding_mask) * 0.8
design_color_features[pred_binding_mask] = 1.0
if self.design:
design_color_features[chain_design_mask] = 0.0
design_color_features[pred_design_mask] = 0.6
# Create a mask to identify unique token-to-res mappings.
# This is for small molecules where multiple tokens can be mapped to the same residue.
token_to_res = prediction["token_to_res"][0].cpu().numpy()
unique_mask = np.ones_like(token_to_res, dtype=bool)
unique_mask[1:] = token_to_res[1:] != token_to_res[:-1]
design_color_features = design_color_features[unique_mask]
open(gen_path, "w").write(
to_mmcif(
structure,
design_coloring=True,
color_features=design_color_features,
)
)
# Write metadata
metadata_path = f"{self.outdir}/{file_name}.npz"
token_mask = sample["token_pad_mask"].bool()
# Build metadata dict with required fields
metadata_dict = {
"design_mask": design_mask[token_mask].cpu().numpy(),
"mol_type": sample["mol_type"][token_mask].cpu().numpy(),
"ss_type": sample["ss_type"][token_mask].cpu().numpy(),
"token_resolved_mask": sample["token_resolved_mask"][token_mask].cpu().numpy(),
"binding_type": binding_type[token_mask].cpu().numpy(),
}
# Add optional fields only if they have valid values (avoid None -> object array)
if "inverse_fold_design_mask" in sample:
metadata_dict["inverse_fold_design_mask"] = (
sample["inverse_fold_design_mask"][token_mask].cpu().numpy()
)
# Per-residue amino acid constraints (for inverse folding step)
# Only save if constraints exist AND have non-zero values
if "aa_constraint_mask" in batch:
aa_mask = batch["aa_constraint_mask"][0]
if aa_mask.any(): # Only save if there are actual constraints
metadata_dict["aa_constraint_mask"] = aa_mask[token_mask].cpu().numpy()
np.savez_compressed(metadata_path, **metadata_dict)
# Write trajectories
if self.save_traj:
trajs = torch.stack(prediction["coords_traj"], dim=1)
traj = trajs[n]
aligned = [traj[0]]
for frame in traj[1:]:
with torch.autocast("cuda", enabled=False):
aligned.append(
weighted_rigid_align(
frame.float().unsqueeze(0),
aligned[-1].float().unsqueeze(0),
sample["atom_pad_mask"].float().unsqueeze(0),
sample["atom_pad_mask"].float().unsqueeze(0),
)
.to(frame)
.squeeze()
)
pdbs = []
all_coords = []
ensemble = []
atom_idx = 0
for idx, frame in tqdm(
enumerate(aligned), desc="Writing traj.", total=len(aligned)
):
sample["coords"] = frame
if self.atom14:
sample = res_from_atom14(sample)
elif self.atom37:
sample = res_from_atom37(sample)
else:
raise ValueError("Either atom14 or atom37 must be true")
str_frame, _, _ = Structure.from_feat(sample)
pdbs.append(to_pdb(str_frame))
all_coords.append(str_frame.coords)
ensemble.append(
(
atom_idx,
len(str_frame.coords),
)
)
atom_idx += len(str_frame.coords)
open(self.outdir / f"{file_name}_traj.pdb", "w").write(
self.combine_pdb_models(pdbs)
)
# Write x0 trajectories
if self.save_x0_traj:
trajs = torch.stack(prediction["x0_coords_traj"], dim=1)
traj = trajs[n]
aligned = [traj[0]]
for frame in traj[1:]:
with torch.autocast("cuda", enabled=False):
aligned.append(
weighted_rigid_align(
frame.float().unsqueeze(0),
aligned[-1].float().unsqueeze(0),
sample["atom_pad_mask"].float().unsqueeze(0),
sample["atom_pad_mask"].float().unsqueeze(0),
)
.to(frame)
.squeeze()
)
pdbs = []
all_coords = []
ensemble = []
atom_idx = 0
for idx, frame in tqdm(
enumerate(aligned), desc="Writing x0 traj.", total=len(aligned)
):
sample["coords"] = frame
if self.atom14:
sample = res_from_atom14(sample)
elif self.atom37:
sample = res_from_atom37(sample)
else:
raise ValueError("Either atom14 or atom37 must be true")
str_frame, _, _ = Structure.from_feat(sample)
pdbs.append(to_pdb(str_frame))
all_coords.append(str_frame.coords)
ensemble.append(
(
atom_idx,
len(str_frame.coords),
)
)
atom_idx += len(str_frame.coords)
open(self.outdir / f"{file_name}_x0_traj.pdb", "w").write(
self.combine_pdb_models(pdbs)
)
except Exception as e: # noqa: BLE001
import traceback
traceback.print_exc() # noqa: T201
msg = f"predict/writer.py: Validation structure writing failed on {batch['id'][0]} with error {e}. Skipping."
print(msg)
def combine_pdb_models(self, pdb_strings):
combined_pdb = ""
model_number = 1
for pdb in pdb_strings:
# Add a model number at the start of each model
combined_pdb += f"MODEL {model_number}\n"
combined_pdb += pdb.split("\nEND")[0]
combined_pdb += "\nENDMDL\n" # End of model marker
model_number += 1
return combined_pdb
def on_predict_epoch_end(
self,
trainer: Trainer, # noqa: ARG002
pl_module: LightningModule, # noqa: ARG002
) -> None:
"""Print the number of failed examples."""
print(f"Number of failed examples: {self.failed}") # noqa: T201
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