DiffDock/inference.py

import copy
import os
import torch
from argparse import ArgumentParser, Namespace
from rdkit.Chem import RemoveHs
from functools import partial
import numpy as np
import pandas as pd
from rdkit import RDLogger
from torch_geometric.loader import DataLoader

from datasets.process_mols import write_mol_with_coords
from utils.diffusion_utils import t_to_sigma as t_to_sigma_compl, get_t_schedule
from utils.inference_utils import InferenceDataset, set_nones
from utils.sampling import randomize_position, sampling
from utils.utils import get_model
from utils.visualise import PDBFile
from tqdm import tqdm

RDLogger.DisableLog('rdApp.*')
import yaml
parser = ArgumentParser()
parser.add_argument('--protein_ligand_csv', type=str, default=None, help='Path to a .csv file specifying the input as described in the README. If this is not None, it will be used instead of the --protein_path, --protein_sequence and --ligand parameters')
parser.add_argument('--complex_name', type=str, default='1a0q', help='Name that the complex will be saved with')
parser.add_argument('--protein_path', type=str, default=None, help='Path to the protein file')
parser.add_argument('--protein_sequence', type=str, default=None, help='Sequence of the protein for ESMFold, this is ignored if --protein_path is not None')
parser.add_argument('--ligand_description', type=str, default='CCCCC(NC(=O)CCC(=O)O)P(=O)(O)OC1=CC=CC=C1', help='Either a SMILES string or the path to a molecule file that rdkit can read')

parser.add_argument('--out_dir', type=str, default='results/user_inference', help='Directory where the outputs will be written to')
parser.add_argument('--save_visualisation', action='store_true', default=False, help='Save a pdb file with all of the steps of the reverse diffusion')
parser.add_argument('--samples_per_complex', type=int, default=10, help='Number of samples to generate')

parser.add_argument('--model_dir', type=str, default='workdir/paper_score_model', help='Path to folder with trained score model and hyperparameters')
parser.add_argument('--ckpt', type=str, default='best_ema_inference_epoch_model.pt', help='Checkpoint to use for the score model')
parser.add_argument('--confidence_model_dir', type=str, default='workdir/paper_confidence_model', help='Path to folder with trained confidence model and hyperparameters')
parser.add_argument('--confidence_ckpt', type=str, default='best_model_epoch75.pt', help='Checkpoint to use for the confidence model')

parser.add_argument('--batch_size', type=int, default=32, help='')
parser.add_argument('--no_final_step_noise', action='store_true', default=False, help='Use no noise in the final step of the reverse diffusion')
parser.add_argument('--inference_steps', type=int, default=20, help='Number of denoising steps')
parser.add_argument('--actual_steps', type=int, default=None, help='Number of denoising steps that are actually performed')
args = parser.parse_args()

os.makedirs(args.out_dir, exist_ok=True)
with open(f'{args.model_dir}/model_parameters.yml') as f:
    score_model_args = Namespace(**yaml.full_load(f))
if args.confidence_model_dir is not None:
    with open(f'{args.confidence_model_dir}/model_parameters.yml') as f:
        confidence_args = Namespace(**yaml.full_load(f))

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

if args.protein_ligand_csv is not None:
    df = pd.read_csv(args.protein_ligand_csv)
    complex_name_list = set_nones(df['complex_name'].tolist())
    protein_path_list = set_nones(df['protein_path'].tolist())
    protein_sequence_list = set_nones(df['protein_sequence'].tolist())
    ligand_description_list = set_nones(df['ligand_description'].tolist())
else:
    complex_name_list = [args.complex_name]
    protein_path_list = [args.protein_path]
    protein_sequence_list = [args.protein_sequence]
    ligand_description_list = [args.ligand_description]

complex_name_list = [name if name is not None else f"complex_{i}" for i, name in enumerate(complex_name_list)]
for name in complex_name_list:
    write_dir = f'{args.out_dir}/{name}'
    os.makedirs(write_dir, exist_ok=True)

# preprocessing of complexes into geometric graphs
test_dataset = InferenceDataset(out_dir=args.out_dir, complex_names=complex_name_list, protein_files=protein_path_list,
                                ligand_descriptions=ligand_description_list, protein_sequences=protein_sequence_list,
                                lm_embeddings=score_model_args.esm_embeddings_path is not None,
                                receptor_radius=score_model_args.receptor_radius, remove_hs=score_model_args.remove_hs,
                                c_alpha_max_neighbors=score_model_args.c_alpha_max_neighbors,
                                all_atoms=score_model_args.all_atoms, atom_radius=score_model_args.atom_radius,
                                atom_max_neighbors=score_model_args.atom_max_neighbors)
test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=False)

if args.confidence_model_dir is not None and not confidence_args.use_original_model_cache:
    print('HAPPENING | confidence model uses different type of graphs than the score model. '
          'Loading (or creating if not existing) the data for the confidence model now.')
    confidence_test_dataset = \
        InferenceDataset(out_dir=args.out_dir, complex_names=complex_name_list, protein_files=protein_path_list,
                         ligand_descriptions=ligand_description_list, protein_sequences=protein_sequence_list,
                         lm_embeddings=confidence_args.esm_embeddings_path is not None,
                         receptor_radius=confidence_args.receptor_radius, remove_hs=confidence_args.remove_hs,
                         c_alpha_max_neighbors=confidence_args.c_alpha_max_neighbors,
                         all_atoms=confidence_args.all_atoms, atom_radius=confidence_args.atom_radius,
                         atom_max_neighbors=confidence_args.atom_max_neighbors,
                         precomputed_lm_embeddings=test_dataset.lm_embeddings)
else:
    confidence_test_dataset = None

t_to_sigma = partial(t_to_sigma_compl, args=score_model_args)

model = get_model(score_model_args, device, t_to_sigma=t_to_sigma, no_parallel=True)
state_dict = torch.load(f'{args.model_dir}/{args.ckpt}', map_location=torch.device('cpu'))
model.load_state_dict(state_dict, strict=True)
model = model.to(device)
model.eval()

if args.confidence_model_dir is not None:
    confidence_model = get_model(confidence_args, device, t_to_sigma=t_to_sigma, no_parallel=True, confidence_mode=True)
    state_dict = torch.load(f'{args.confidence_model_dir}/{args.confidence_ckpt}', map_location=torch.device('cpu'))
    confidence_model.load_state_dict(state_dict, strict=True)
    confidence_model = confidence_model.to(device)
    confidence_model.eval()
else:
    confidence_model = None
    confidence_args = None

tr_schedule = get_t_schedule(inference_steps=args.inference_steps)

failures, skipped = 0, 0
N = args.samples_per_complex
print('Size of test dataset: ', len(test_dataset))
for idx, orig_complex_graph in tqdm(enumerate(test_loader)):
    if not orig_complex_graph.success[0]:
        skipped += 1
        print(f"HAPPENING | The test dataset did not contain {test_dataset.complex_names[idx]} for {test_dataset.ligand_descriptions[idx]} and {test_dataset.protein_files[idx]}. We are skipping this complex.")
        continue
    try:
        if confidence_test_dataset is not None:
            confidence_complex_graph = confidence_test_dataset[idx]
            if not confidence_complex_graph.success:
                skipped += 1
                print(f"HAPPENING | The confidence dataset did not contain {orig_complex_graph.name}. We are skipping this complex.")
                continue
            confidence_data_list = [copy.deepcopy(confidence_complex_graph) for _ in range(N)]
        else:
            confidence_data_list = None
        data_list = [copy.deepcopy(orig_complex_graph) for _ in range(N)]
        randomize_position(data_list, score_model_args.no_torsion, False, score_model_args.tr_sigma_max)
        lig = orig_complex_graph.mol[0]

        # initialize visualisation
        pdb = None
        if args.save_visualisation:
            visualization_list = []
            for graph in data_list:
                pdb = PDBFile(lig)
                pdb.add(lig, 0, 0)
                pdb.add((orig_complex_graph['ligand'].pos + orig_complex_graph.original_center).detach().cpu(), 1, 0)
                pdb.add((graph['ligand'].pos + graph.original_center).detach().cpu(), part=1, order=1)
                visualization_list.append(pdb)
        else:
            visualization_list = None

        # run reverse diffusion
        data_list, confidence = sampling(data_list=data_list, model=model,
                                         inference_steps=args.actual_steps if args.actual_steps is not None else args.inference_steps,
                                         tr_schedule=tr_schedule, rot_schedule=tr_schedule, tor_schedule=tr_schedule,
                                         device=device, t_to_sigma=t_to_sigma, model_args=score_model_args,
                                         visualization_list=visualization_list, confidence_model=confidence_model,
                                         confidence_data_list=confidence_data_list, confidence_model_args=confidence_args,
                                         batch_size=args.batch_size, no_final_step_noise=args.no_final_step_noise)
        ligand_pos = np.asarray([complex_graph['ligand'].pos.cpu().numpy() + orig_complex_graph.original_center.cpu().numpy() for complex_graph in data_list])

        # reorder predictions based on confidence output
        if confidence is not None and isinstance(confidence_args.rmsd_classification_cutoff, list):
            confidence = confidence[:, 0]
        if confidence is not None:
            confidence = confidence.cpu().numpy()
            re_order = np.argsort(confidence)[::-1]
            confidence = confidence[re_order]
            ligand_pos = ligand_pos[re_order]

        # save predictions
        write_dir = f'{args.out_dir}/{complex_name_list[idx]}'
        for rank, pos in enumerate(ligand_pos):
            mol_pred = copy.deepcopy(lig)
            if score_model_args.remove_hs: mol_pred = RemoveHs(mol_pred)
            if rank == 0: write_mol_with_coords(mol_pred, pos, os.path.join(write_dir, f'rank{rank+1}.sdf'))
            write_mol_with_coords(mol_pred, pos, os.path.join(write_dir, f'rank{rank+1}_confidence{confidence[rank]:.2f}.sdf'))

        # save visualisation frames
        if args.save_visualisation:
            if confidence is not None:
                for rank, batch_idx in enumerate(re_order):
                    visualization_list[batch_idx].write(os.path.join(write_dir, f'rank{rank+1}_reverseprocess.pdb'))
            else:
                for rank, batch_idx in enumerate(ligand_pos):
                    visualization_list[batch_idx].write(os.path.join(write_dir, f'rank{rank+1}_reverseprocess.pdb'))

    except Exception as e:
        print("Failed on", orig_complex_graph["name"], e)
        failures += 1

print(f'Failed for {failures} complexes')
print(f'Skipped {skipped} complexes')
print(f'Results are in {args.out_dir}')