import os import subprocess import warnings from datetime import datetime from typing import List import numpy import numpy as np import torch import yaml from rdkit import Chem from rdkit.Chem import RemoveHs, MolToPDBFile from torch import nn, Tensor from torch_geometric.nn.data_parallel import DataParallel from torch_geometric.utils import degree, subgraph from models.aa_model import AAModel from models.cg_model import CGModel from models.old_aa_model import AAOldModel from models.old_cg_model import CGOldModel from utils.diffusion_utils import get_timestep_embedding 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 unbatch(src, batch: Tensor, dim: int = 0) -> List[Tensor]: r"""Splits :obj:`src` according to a :obj:`batch` vector along dimension :obj:`dim`. Args: src (Tensor): The source tensor. batch (LongTensor): The batch vector :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each entry in :obj:`src` to a specific example. Must be ordered. dim (int, optional): The dimension along which to split the :obj:`src` tensor. (default: :obj:`0`) :rtype: :class:`List[Tensor]` """ sizes = degree(batch, dtype=torch.long).tolist() if isinstance(src, numpy.ndarray): return np.split(src, np.array(sizes).cumsum()[:-1], axis=dim) else: return src.split(sizes, dim) def unbatch_edge_index(edge_index: Tensor, batch: Tensor) -> List[Tensor]: r"""Splits the :obj:`edge_index` according to a :obj:`batch` vector. Args: edge_index (Tensor): The edge_index tensor. Must be ordered. batch (LongTensor): The batch vector :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each node to a specific example. Must be ordered. :rtype: :class:`List[Tensor]` """ deg = degree(batch, dtype=torch.int64) ptr = torch.cat([deg.new_zeros(1), deg.cumsum(dim=0)[:-1]], dim=0) edge_batch = batch[edge_index[0]] edge_index = edge_index - ptr[edge_batch] sizes = degree(edge_batch, dtype=torch.int64).cpu().tolist() return edge_index.split(sizes, dim=1) def unbatch_edge_attributes(edge_attributes, edge_index: Tensor, batch: Tensor) -> List[Tensor]: edge_batch = batch[edge_index[0]] sizes = degree(edge_batch, dtype=torch.int64).cpu().tolist() return edge_attributes.split(sizes, dim=0) 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 unfreeze_layer(model): for name, child in (model.named_children()): #print(name, child.parameters()) for param in child.parameters(): param.requires_grad = True def get_optimizer_and_scheduler(args, model, scheduler_mode='min', step=0, optimizer=None): if args.scheduler == 'layer_linear_warmup': if step == 0: for name, child in (model.named_children()): if name.find('batch_norm') == -1: for name, param in child.named_parameters(): if name.find('batch_norm') == -1: param.requires_grad = False for l in [model.center_edge_embedding, model.final_conv, model.tr_final_layer, model.rot_final_layer, model.final_edge_embedding, model.final_tp_tor, model.tor_bond_conv, model.tor_final_layer]: unfreeze_layer(l) elif 0 < step <= args.num_conv_layers: unfreeze_layer(model.conv_layers[-step]) elif step == args.num_conv_layers + 1: for l in [model.lig_node_embedding, model.lig_edge_embedding, model.rec_node_embedding, model.rec_edge_embedding, model.rec_sigma_embedding, model.cross_edge_embedding, model.rec_emb_layers, model.lig_emb_layers]: unfreeze_layer(l) if step == 0 or args.scheduler == 'layer_linear_warmup': optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, weight_decay=args.w_decay) scheduler_plateau = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=scheduler_mode, factor=0.7, patience=args.scheduler_patience, min_lr=args.lr / 100) if args.scheduler == 'plateau': scheduler = scheduler_plateau elif args.scheduler == 'linear_warmup' or args.scheduler == 'layer_linear_warmup': if (args.scheduler == 'linear_warmup' and step < 1) or \ (args.scheduler == 'layer_linear_warmup' and step <= args.num_conv_layers + 1): scheduler = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=args.lr_start_factor, end_factor=1.0, total_iters=args.warmup_dur) else: scheduler = scheduler_plateau else: print('No scheduler') scheduler = None return optimizer, scheduler def get_model(args, device, t_to_sigma, no_parallel=False, confidence_mode=False, old=False): timestep_emb_func = get_timestep_embedding( embedding_type=args.embedding_type if 'embedding_type' in args else 'sinusoidal', embedding_dim=args.sigma_embed_dim, embedding_scale=args.embedding_scale if 'embedding_type' in args else 10000) if old: if 'all_atoms' in args and args.all_atoms: model_class = AAOldModel else: model_class = CGOldModel 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, norm_by_sigma='norm_by_sigma' in args and args.norm_by_sigma, 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, smooth_edges=args.smooth_edges if "smooth_edges" in args else False, odd_parity=args.odd_parity if "odd_parity" in args else False, lm_embedding_type=lm_embedding_type, confidence_mode=confidence_mode, affinity_prediction=args.affinity_prediction if 'affinity_prediction' in args else False, parallel=args.parallel if "parallel" in args else 1, num_confidence_outputs=len( args.rmsd_classification_cutoff) + 1 if 'rmsd_classification_cutoff' in args and isinstance( args.rmsd_classification_cutoff, list) else 1, parallel_aggregators=args.parallel_aggregators if "parallel_aggregators" in args else "", fixed_center_conv=not args.not_fixed_center_conv if "not_fixed_center_conv" in args else False, no_aminoacid_identities=args.no_aminoacid_identities if "no_aminoacid_identities" in args else False, include_miscellaneous_atoms=args.include_miscellaneous_atoms if hasattr(args, 'include_miscellaneous_atoms') else False, use_old_atom_encoder=args.use_old_atom_encoder if hasattr(args, 'use_old_atom_encoder') else True) else: if 'all_atoms' in args and args.all_atoms: model_class = AAModel else: model_class = CGModel lm_embedding_type = None if ('moad_esm_embeddings_path' in args and args.moad_esm_embeddings_path is not None) or \ ('pdbbind_esm_embeddings_path' in args and args.pdbbind_esm_embeddings_path is not None) or \ ('pdbsidechain_esm_embeddings_path' in args and args.pdbsidechain_esm_embeddings_path is not None) or \ ('esm_embeddings_path' in args and args.esm_embeddings_path is not None): lm_embedding_type = 'precomputed' if 'esm_embeddings_model' in args and args.esm_embeddings_model is not None: lm_embedding_type = args.esm_embeddings_model 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, norm_by_sigma='norm_by_sigma' in args and args.norm_by_sigma, 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, smooth_edges=args.smooth_edges if "smooth_edges" in args else False, odd_parity=args.odd_parity if "odd_parity" in args else False, lm_embedding_type=lm_embedding_type, confidence_mode=confidence_mode, affinity_prediction=args.affinity_prediction if 'affinity_prediction' in args else False, parallel=args.parallel if "parallel" in args else 1, num_confidence_outputs=len( args.rmsd_classification_cutoff) + 1 if 'rmsd_classification_cutoff' in args and isinstance( args.rmsd_classification_cutoff, list) else 1, atom_num_confidence_outputs=len( args.atom_rmsd_classification_cutoff) + 1 if 'atom_rmsd_classification_cutoff' in args and isinstance( args.atom_rmsd_classification_cutoff, list) else 1, parallel_aggregators=args.parallel_aggregators if "parallel_aggregators" in args else "", fixed_center_conv=not args.not_fixed_center_conv if "not_fixed_center_conv" in args else False, no_aminoacid_identities=args.no_aminoacid_identities if "no_aminoacid_identities" in args else False, include_miscellaneous_atoms=args.include_miscellaneous_atoms if hasattr(args, 'include_miscellaneous_atoms') else False, sh_lmax=args.sh_lmax if 'sh_lmax' in args else 2, differentiate_convolutions=not args.no_differentiate_convolutions if "no_differentiate_convolutions" in args else True, tp_weights_layers=args.tp_weights_layers if "tp_weights_layers" in args else 2, num_prot_emb_layers=args.num_prot_emb_layers if "num_prot_emb_layers" in args else 0, reduce_pseudoscalars=args.reduce_pseudoscalars if "reduce_pseudoscalars" in args else False, embed_also_ligand=args.embed_also_ligand if "embed_also_ligand" in args else False, atom_confidence=args.atom_confidence_loss_weight > 0.0 if "atom_confidence_loss_weight" in args else False, sidechain_pred=(hasattr(args, 'sidechain_loss_weight') and args.sidechain_loss_weight > 0) or (hasattr(args, 'backbone_loss_weight') and args.backbone_loss_weight > 0), depthwise_convolution=args.depthwise_convolution if hasattr(args, 'depthwise_convolution') else False) if device.type == 'cuda' and not no_parallel and ('dataset' not in args or not args.dataset == 'torsional'): model = DataParallel(model) model.to(device) return model import signal from contextlib import contextmanager 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']] def crop_beyond(complex_graph, cutoff, all_atoms): ligand_pos = complex_graph['ligand'].pos receptor_pos = complex_graph['receptor'].pos residues_to_keep = torch.any(torch.sum((ligand_pos.unsqueeze(0) - receptor_pos.unsqueeze(1)) ** 2, -1) < cutoff ** 2, dim=1) if all_atoms: #print(complex_graph['atom'].x.shape, complex_graph['atom'].pos.shape, complex_graph['atom', 'atom_rec_contact', 'receptor'].edge_index.shape) atom_to_res_mapping = complex_graph['atom', 'atom_rec_contact', 'receptor'].edge_index[1] atoms_to_keep = residues_to_keep[atom_to_res_mapping] rec_remapper = (torch.cumsum(residues_to_keep.long(), dim=0) - 1) atom_to_res_new_mapping = rec_remapper[atom_to_res_mapping][atoms_to_keep] atom_res_edge_index = torch.stack([torch.arange(len(atom_to_res_new_mapping), device=atom_to_res_new_mapping.device), atom_to_res_new_mapping]) complex_graph['receptor'].pos = complex_graph['receptor'].pos[residues_to_keep] complex_graph['receptor'].x = complex_graph['receptor'].x[residues_to_keep] complex_graph['receptor'].side_chain_vecs = complex_graph['receptor'].side_chain_vecs[residues_to_keep] complex_graph['receptor', 'rec_contact', 'receptor'].edge_index = \ subgraph(residues_to_keep, complex_graph['receptor', 'rec_contact', 'receptor'].edge_index, relabel_nodes=True)[0] if all_atoms: complex_graph['atom'].x = complex_graph['atom'].x[atoms_to_keep] complex_graph['atom'].pos = complex_graph['atom'].pos[atoms_to_keep] complex_graph['atom', 'atom_contact', 'atom'].edge_index = subgraph(atoms_to_keep, complex_graph['atom', 'atom_contact', 'atom'].edge_index, relabel_nodes=True)[0] complex_graph['atom', 'atom_rec_contact', 'receptor'].edge_index = atom_res_edge_index #print("cropped", 1-torch.mean(residues_to_keep.float()), 'residues', 1-torch.mean(atoms_to_keep.float()), 'atoms')