import os import subprocess import warnings from datetime import datetime import signal from contextlib import contextmanager import numpy as np import torch import yaml from rdkit import Chem from rdkit.Chem import RemoveHs, MolToPDBFile from torch_geometric.nn.data_parallel import DataParallel from models.all_atom_score_model import TensorProductScoreModel as AAScoreModel from models.score_model import TensorProductScoreModel as CGScoreModel from utils.diffusion_utils import get_timestep_embedding from spyrmsd import rmsd, molecule def get_obrmsd(mol1_path, mol2_path, cache_name=None): cache_name = datetime.now().strftime('date%d-%m_time%H-%M-%S.%f') if cache_name is None else cache_name os.makedirs(".openbabel_cache", exist_ok=True) if not isinstance(mol1_path, str): MolToPDBFile(mol1_path, '.openbabel_cache/obrmsd_mol1_cache.pdb') mol1_path = '.openbabel_cache/obrmsd_mol1_cache.pdb' if not isinstance(mol2_path, str): MolToPDBFile(mol2_path, '.openbabel_cache/obrmsd_mol2_cache.pdb') mol2_path = '.openbabel_cache/obrmsd_mol2_cache.pdb' with warnings.catch_warnings(): warnings.simplefilter("ignore") return_code = subprocess.run(f"obrms {mol1_path} {mol2_path} > .openbabel_cache/obrmsd_{cache_name}.rmsd", shell=True) print(return_code) obrms_output = read_strings_from_txt(f".openbabel_cache/obrmsd_{cache_name}.rmsd") rmsds = [line.split(" ")[-1] for line in obrms_output] return np.array(rmsds, dtype=np.float) def remove_all_hs(mol): params = Chem.RemoveHsParameters() params.removeAndTrackIsotopes = True params.removeDefiningBondStereo = True params.removeDegreeZero = True params.removeDummyNeighbors = True params.removeHigherDegrees = True params.removeHydrides = True params.removeInSGroups = True params.removeIsotopes = True params.removeMapped = True params.removeNonimplicit = True params.removeOnlyHNeighbors = True params.removeWithQuery = True params.removeWithWedgedBond = True return RemoveHs(mol, params) def read_strings_from_txt(path): # every line will be one element of the returned list with open(path) as file: lines = file.readlines() return [line.rstrip() for line in lines] def save_yaml_file(path, content): assert isinstance(path, str), f'path must be a string, got {path} which is a {type(path)}' content = yaml.dump(data=content) if '/' in path and os.path.dirname(path) and not os.path.exists(os.path.dirname(path)): os.makedirs(os.path.dirname(path)) with open(path, 'w') as f: f.write(content) def get_optimizer_and_scheduler(args, model, scheduler_mode='min'): optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.w_decay) if args.scheduler == 'plateau': scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=scheduler_mode, factor=0.7, patience=args.scheduler_patience, min_lr=args.lr / 100) else: print('No scheduler') scheduler = None return optimizer, scheduler def get_model(args, device, t_to_sigma, no_parallel=False, confidence_mode=False): if 'all_atoms' in args and args.all_atoms: model_class = AAScoreModel else: model_class = CGScoreModel timestep_emb_func = get_timestep_embedding( embedding_type=args.embedding_type, embedding_dim=args.sigma_embed_dim, embedding_scale=args.embedding_scale) lm_embedding_type = None if args.esm_embeddings_path is not None: lm_embedding_type = 'esm' model = model_class(t_to_sigma=t_to_sigma, device=device, no_torsion=args.no_torsion, timestep_emb_func=timestep_emb_func, num_conv_layers=args.num_conv_layers, lig_max_radius=args.max_radius, scale_by_sigma=args.scale_by_sigma, sigma_embed_dim=args.sigma_embed_dim, ns=args.ns, nv=args.nv, distance_embed_dim=args.distance_embed_dim, cross_distance_embed_dim=args.cross_distance_embed_dim, batch_norm=not args.no_batch_norm, dropout=args.dropout, use_second_order_repr=args.use_second_order_repr, cross_max_distance=args.cross_max_distance, dynamic_max_cross=args.dynamic_max_cross, lm_embedding_type=lm_embedding_type, confidence_mode=confidence_mode, num_confidence_outputs=len( args.rmsd_classification_cutoff) + 1 if 'rmsd_classification_cutoff' in args and isinstance( args.rmsd_classification_cutoff, list) else 1) if device.type == 'cuda' and not no_parallel: model = DataParallel(model) model.to(device) return model def get_symmetry_rmsd(mol, coords1, coords2, mol2=None): with time_limit(10): mol = molecule.Molecule.from_rdkit(mol) mol2 = molecule.Molecule.from_rdkit(mol2) if mol2 is not None else mol2 mol2_atomicnums = mol2.atomicnums if mol2 is not None else mol.atomicnums mol2_adjacency_matrix = mol2.adjacency_matrix if mol2 is not None else mol.adjacency_matrix RMSD = rmsd.symmrmsd( coords1, coords2, mol.atomicnums, mol2_atomicnums, mol.adjacency_matrix, mol2_adjacency_matrix, ) return RMSD class TimeoutException(Exception): pass @contextmanager def time_limit(seconds): def signal_handler(signum, frame): raise TimeoutException("Timed out!") signal.signal(signal.SIGALRM, signal_handler) signal.alarm(seconds) try: yield finally: signal.alarm(0) class ExponentialMovingAverage: """ from https://github.com/yang-song/score_sde_pytorch/blob/main/models/ema.py Maintains (exponential) moving average of a set of parameters. """ def __init__(self, parameters, decay, use_num_updates=True): """ Args: parameters: Iterable of `torch.nn.Parameter`; usually the result of `model.parameters()`. decay: The exponential decay. use_num_updates: Whether to use number of updates when computing averages. """ if decay < 0.0 or decay > 1.0: raise ValueError('Decay must be between 0 and 1') self.decay = decay self.num_updates = 0 if use_num_updates else None self.shadow_params = [p.clone().detach() for p in parameters if p.requires_grad] self.collected_params = [] def update(self, parameters): """ Update currently maintained parameters. Call this every time the parameters are updated, such as the result of the `optimizer.step()` call. Args: parameters: Iterable of `torch.nn.Parameter`; usually the same set of parameters used to initialize this object. """ decay = self.decay if self.num_updates is not None: self.num_updates += 1 decay = min(decay, (1 + self.num_updates) / (10 + self.num_updates)) one_minus_decay = 1.0 - decay with torch.no_grad(): parameters = [p for p in parameters if p.requires_grad] for s_param, param in zip(self.shadow_params, parameters): s_param.sub_(one_minus_decay * (s_param - param)) def copy_to(self, parameters): """ Copy current parameters into given collection of parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored moving averages. """ parameters = [p for p in parameters if p.requires_grad] for s_param, param in zip(self.shadow_params, parameters): if param.requires_grad: param.data.copy_(s_param.data) def store(self, parameters): """ Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored. """ self.collected_params = [param.clone() for param in parameters] def restore(self, parameters): """ Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the original optimization process. Store the parameters before the `copy_to` method. After validation (or model saving), use this to restore the former parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored parameters. """ for c_param, param in zip(self.collected_params, parameters): param.data.copy_(c_param.data) def state_dict(self): return dict(decay=self.decay, num_updates=self.num_updates, shadow_params=self.shadow_params) def load_state_dict(self, state_dict, device): self.decay = state_dict['decay'] self.num_updates = state_dict['num_updates'] self.shadow_params = [tensor.to(device) for tensor in state_dict['shadow_params']]