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| import math | |
| from e3nn import o3 | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from torch_cluster import radius, radius_graph | |
| from torch_scatter import scatter, scatter_mean | |
| import numpy as np | |
| from e3nn.nn import BatchNorm | |
| from models.layers import OldAtomEncoder, AtomEncoder, GaussianSmearing | |
| from models.tensor_layers import OldTensorProductConvLayer | |
| from utils import so3, torus | |
| from datasets.process_mols import lig_feature_dims, rec_residue_feature_dims, rec_atom_feature_dims | |
| class CGOldModel(torch.nn.Module): | |
| def __init__(self, t_to_sigma, device, timestep_emb_func, in_lig_edge_features=4, sigma_embed_dim=32, sh_lmax=2, | |
| ns=16, nv=4, num_conv_layers=2, lig_max_radius=5, rec_max_radius=30, cross_max_distance=250, | |
| center_max_distance=30, distance_embed_dim=32, cross_distance_embed_dim=32, no_torsion=False, | |
| scale_by_sigma=True, norm_by_sigma=True, use_second_order_repr=False, batch_norm=True, | |
| dynamic_max_cross=False, dropout=0.0, smooth_edges=False, odd_parity=False, | |
| separate_noise_schedule=False, lm_embedding_type=None, confidence_mode=False, | |
| confidence_dropout=0, confidence_no_batchnorm=False, | |
| asyncronous_noise_schedule=False, affinity_prediction=False, parallel=1, | |
| parallel_aggregators="mean max min std", num_confidence_outputs=1, fixed_center_conv=False, | |
| no_aminoacid_identities=False, include_miscellaneous_atoms=False, use_old_atom_encoder=False): | |
| super(CGOldModel, self).__init__() | |
| assert parallel == 1, "not implemented" | |
| assert (not no_aminoacid_identities) or (lm_embedding_type is None), "no language model emb without identities" | |
| self.t_to_sigma = t_to_sigma | |
| self.in_lig_edge_features = in_lig_edge_features | |
| sigma_embed_dim *= (3 if separate_noise_schedule else 1) | |
| self.sigma_embed_dim = sigma_embed_dim | |
| self.lig_max_radius = lig_max_radius | |
| self.rec_max_radius = rec_max_radius | |
| self.include_miscellaneous_atoms = include_miscellaneous_atoms | |
| self.cross_max_distance = cross_max_distance | |
| self.dynamic_max_cross = dynamic_max_cross | |
| self.center_max_distance = center_max_distance | |
| self.distance_embed_dim = distance_embed_dim | |
| self.cross_distance_embed_dim = cross_distance_embed_dim | |
| self.sh_irreps = o3.Irreps.spherical_harmonics(lmax=sh_lmax) | |
| self.ns, self.nv = ns, nv | |
| self.scale_by_sigma = scale_by_sigma | |
| self.norm_by_sigma = norm_by_sigma | |
| self.device = device | |
| self.no_torsion = no_torsion | |
| self.smooth_edges = smooth_edges | |
| self.odd_parity = odd_parity | |
| self.timestep_emb_func = timestep_emb_func | |
| self.separate_noise_schedule = separate_noise_schedule | |
| self.confidence_mode = confidence_mode | |
| self.num_conv_layers = num_conv_layers | |
| self.asyncronous_noise_schedule = asyncronous_noise_schedule | |
| self.affinity_prediction = affinity_prediction | |
| self.fixed_center_conv = fixed_center_conv | |
| self.no_aminoacid_identities = no_aminoacid_identities | |
| atom_encoder_class = OldAtomEncoder if use_old_atom_encoder else AtomEncoder | |
| self.lig_node_embedding = atom_encoder_class(emb_dim=ns, feature_dims=lig_feature_dims, sigma_embed_dim=sigma_embed_dim) | |
| self.lig_edge_embedding = nn.Sequential(nn.Linear(in_lig_edge_features + sigma_embed_dim + distance_embed_dim, ns),nn.ReLU(),nn.Dropout(dropout),nn.Linear(ns, ns)) | |
| self.rec_node_embedding = atom_encoder_class(emb_dim=ns, feature_dims=rec_residue_feature_dims, sigma_embed_dim=sigma_embed_dim, lm_embedding_type=lm_embedding_type) | |
| self.rec_edge_embedding = nn.Sequential(nn.Linear(sigma_embed_dim + distance_embed_dim, ns), nn.ReLU(), nn.Dropout(dropout),nn.Linear(ns, ns)) | |
| if self.include_miscellaneous_atoms: | |
| self.misc_atom_node_embedding = atom_encoder_class(emb_dim=ns, feature_dims=rec_atom_feature_dims, sigma_embed_dim=sigma_embed_dim) | |
| self.misc_atom_edge_embedding = nn.Sequential(nn.Linear(sigma_embed_dim + distance_embed_dim, ns), nn.ReLU(),nn.Dropout(dropout), nn.Linear(ns, ns)) | |
| self.ar_edge_embedding = nn.Sequential(nn.Linear(sigma_embed_dim + distance_embed_dim, ns), nn.ReLU(),nn.Dropout(dropout), nn.Linear(ns, ns)) | |
| self.la_edge_embedding = nn.Sequential(nn.Linear(sigma_embed_dim + cross_distance_embed_dim, ns), nn.ReLU(),nn.Dropout(dropout), nn.Linear(ns, ns)) | |
| self.cross_edge_embedding = nn.Sequential(nn.Linear(sigma_embed_dim + cross_distance_embed_dim, ns), nn.ReLU(), nn.Dropout(dropout),nn.Linear(ns, ns)) | |
| self.lig_distance_expansion = GaussianSmearing(0.0, lig_max_radius, distance_embed_dim) | |
| self.rec_distance_expansion = GaussianSmearing(0.0, rec_max_radius, distance_embed_dim) | |
| self.cross_distance_expansion = GaussianSmearing(0.0, cross_max_distance, cross_distance_embed_dim) | |
| if use_second_order_repr: | |
| irrep_seq = [ | |
| f'{ns}x0e', | |
| f'{ns}x0e + {nv}x1o + {nv}x2e', | |
| f'{ns}x0e + {nv}x1o + {nv}x2e + {nv}x1e + {nv}x2o', | |
| f'{ns}x0e + {nv}x1o + {nv}x2e + {nv}x1e + {nv}x2o + {ns}x0o' | |
| ] | |
| else: | |
| irrep_seq = [ | |
| f'{ns}x0e', | |
| f'{ns}x0e + {nv}x1o', | |
| f'{ns}x0e + {nv}x1o + {nv}x1e', | |
| f'{ns}x0e + {nv}x1o + {nv}x1e + {ns}x0o' | |
| ] | |
| lig_conv_layers, rec_conv_layers, lig_to_rec_conv_layers, rec_to_lig_conv_layers = [], [], [], [] | |
| if self.include_miscellaneous_atoms: | |
| misc_conv_layers, la_conv_layers, ra_conv_layers, al_conv_layers, ar_conv_layers = [], [], [], [], [] | |
| for i in range(num_conv_layers): | |
| in_irreps = irrep_seq[min(i, len(irrep_seq) - 1)] | |
| out_irreps = irrep_seq[min(i + 1, len(irrep_seq) - 1)] | |
| parameters = { | |
| 'in_irreps': in_irreps, | |
| 'sh_irreps': self.sh_irreps, | |
| 'out_irreps': out_irreps, | |
| 'n_edge_features': 3 * ns, | |
| 'hidden_features': 3 * ns, | |
| 'residual': False, | |
| 'batch_norm': batch_norm, | |
| 'dropout': dropout | |
| } | |
| lig_layer = OldTensorProductConvLayer(**parameters) | |
| lig_conv_layers.append(lig_layer) | |
| rec_layer = OldTensorProductConvLayer(**parameters) | |
| rec_conv_layers.append(rec_layer) | |
| lig_to_rec_layer = OldTensorProductConvLayer(**parameters) | |
| lig_to_rec_conv_layers.append(lig_to_rec_layer) | |
| rec_to_lig_layer = OldTensorProductConvLayer(**parameters) | |
| rec_to_lig_conv_layers.append(rec_to_lig_layer) | |
| if self.include_miscellaneous_atoms: | |
| misc_conv_layer = OldTensorProductConvLayer(**parameters) | |
| la_conv_layer = OldTensorProductConvLayer(**parameters) | |
| ra_conv_layer = OldTensorProductConvLayer(**parameters) | |
| al_conv_layer = OldTensorProductConvLayer(**parameters) | |
| ar_conv_layer = OldTensorProductConvLayer(**parameters) | |
| misc_conv_layers.append(misc_conv_layer) | |
| la_conv_layers.append(la_conv_layer) | |
| ra_conv_layers.append(ra_conv_layer) | |
| al_conv_layers.append(al_conv_layer) | |
| ar_conv_layers.append(ar_conv_layer) | |
| self.lig_conv_layers = nn.ModuleList(lig_conv_layers) | |
| self.rec_conv_layers = nn.ModuleList(rec_conv_layers) | |
| self.lig_to_rec_conv_layers = nn.ModuleList(lig_to_rec_conv_layers) | |
| self.rec_to_lig_conv_layers = nn.ModuleList(rec_to_lig_conv_layers) | |
| if self.include_miscellaneous_atoms: | |
| self.misc_conv_layers = nn.ModuleList(misc_conv_layers) | |
| self.la_conv_layers = nn.ModuleList(la_conv_layers) | |
| self.ra_conv_layers = nn.ModuleList(ra_conv_layers) | |
| self.al_conv_layers = nn.ModuleList(al_conv_layers) | |
| self.ar_conv_layers = nn.ModuleList(ar_conv_layers) | |
| if self.confidence_mode: | |
| self.confidence_predictor = nn.Sequential( | |
| nn.Linear(2*self.ns if num_conv_layers >= 3 else self.ns,ns), | |
| nn.BatchNorm1d(ns) if not confidence_no_batchnorm else nn.Identity(), | |
| nn.ReLU(), | |
| nn.Dropout(confidence_dropout), | |
| nn.Linear(ns, ns), | |
| nn.BatchNorm1d(ns) if not confidence_no_batchnorm else nn.Identity(), | |
| nn.ReLU(), | |
| nn.Dropout(confidence_dropout), | |
| nn.Linear(ns, 2 if self.affinity_prediction else 1) | |
| ) | |
| else: | |
| # center of mass translation and rotation components | |
| self.center_distance_expansion = GaussianSmearing(0.0, center_max_distance, distance_embed_dim) | |
| self.center_edge_embedding = nn.Sequential( | |
| nn.Linear(distance_embed_dim + sigma_embed_dim, ns), | |
| nn.ReLU(), | |
| nn.Dropout(dropout), | |
| nn.Linear(ns, ns) | |
| ) | |
| self.final_conv = OldTensorProductConvLayer( | |
| in_irreps=self.lig_conv_layers[-1].out_irreps, | |
| sh_irreps=self.sh_irreps, | |
| out_irreps=f'2x1o + 2x1e' if not self.odd_parity else '1x1o + 1x1e', | |
| n_edge_features=2 * ns, | |
| residual=False, | |
| dropout=dropout, | |
| batch_norm=batch_norm | |
| ) | |
| self.tr_final_layer = nn.Sequential(nn.Linear(1 + sigma_embed_dim, ns),nn.Dropout(dropout), nn.ReLU(), nn.Linear(ns, 1)) | |
| self.rot_final_layer = nn.Sequential(nn.Linear(1 + sigma_embed_dim, ns),nn.Dropout(dropout), nn.ReLU(), nn.Linear(ns, 1)) | |
| if not no_torsion: | |
| # torsion angles components | |
| self.final_edge_embedding = nn.Sequential( | |
| nn.Linear(distance_embed_dim, ns), | |
| nn.ReLU(), | |
| nn.Dropout(dropout), | |
| nn.Linear(ns, ns) | |
| ) | |
| self.final_tp_tor = o3.FullTensorProduct(self.sh_irreps, "2e") | |
| self.tor_bond_conv = OldTensorProductConvLayer( | |
| in_irreps=self.lig_conv_layers[-1].out_irreps, | |
| sh_irreps=self.final_tp_tor.irreps_out, | |
| out_irreps=f'{ns}x0o + {ns}x0e' if not self.odd_parity else f'{ns}x0o', | |
| n_edge_features=3 * ns, | |
| residual=False, | |
| dropout=dropout, | |
| batch_norm=batch_norm | |
| ) | |
| self.tor_final_layer = nn.Sequential( | |
| nn.Linear(2 * ns if not self.odd_parity else ns, ns, bias=False), | |
| nn.Tanh(), | |
| nn.Dropout(dropout), | |
| nn.Linear(ns, 1, bias=False) | |
| ) | |
| def forward(self, data): | |
| if self.no_aminoacid_identities: | |
| data['receptor'].x = data['receptor'].x * 0 | |
| if not self.confidence_mode: | |
| tr_sigma, rot_sigma, tor_sigma = self.t_to_sigma(*[data.complex_t[noise_type] for noise_type in ['tr', 'rot', 'tor']]) | |
| else: | |
| tr_sigma, rot_sigma, tor_sigma = [data.complex_t[noise_type] for noise_type in ['tr', 'rot', 'tor']] | |
| # build ligand graph | |
| lig_node_attr, lig_edge_index, lig_edge_attr, lig_edge_sh, lig_edge_weight = self.build_lig_conv_graph(data) | |
| lig_src, lig_dst = lig_edge_index | |
| lig_node_attr = self.lig_node_embedding(lig_node_attr) | |
| lig_edge_attr = self.lig_edge_embedding(lig_edge_attr) | |
| # build receptor graph | |
| rec_node_attr, rec_edge_index, rec_edge_attr, rec_edge_sh, rec_edge_weight = self.build_rec_conv_graph(data) | |
| rec_src, rec_dst = rec_edge_index | |
| rec_node_attr = self.rec_node_embedding(rec_node_attr) | |
| rec_edge_attr = self.rec_edge_embedding(rec_edge_attr) | |
| if self.include_miscellaneous_atoms: | |
| # build misc_atom graph | |
| atom_node_attr, atom_edge_index, atom_edge_attr, atom_edge_sh, atom_edge_weight = self.build_misc_atom_conv_graph(data) | |
| atom_node_attr = self.misc_atom_node_embedding(atom_node_attr) | |
| atom_edge_attr = self.misc_atom_edge_embedding(atom_edge_attr) | |
| # build cross graph | |
| if self.dynamic_max_cross: | |
| cross_cutoff = (tr_sigma * 3 + 20).unsqueeze(1) | |
| else: | |
| cross_cutoff = self.cross_max_distance | |
| if self.include_miscellaneous_atoms: | |
| lr_edge_index, lr_edge_attr, lr_edge_sh, lr_edge_weight, la_edge_index, la_edge_attr, \ | |
| la_edge_sh, la_edge_weight, ar_edge_index, ar_edge_attr, ar_edge_sh, ar_edge_weight = \ | |
| self.build_misc_cross_conv_graph(data, cross_cutoff) | |
| lr_edge_attr = self.cross_edge_embedding(lr_edge_attr) | |
| la_edge_attr = self.la_edge_embedding(la_edge_attr) | |
| ar_edge_attr = self.ar_edge_embedding(ar_edge_attr) | |
| cross_lig, cross_rec = lr_edge_index | |
| else: | |
| lr_edge_index, lr_edge_attr, lr_edge_sh, lr_edge_weight = self.build_cross_conv_graph(data, cross_cutoff) | |
| cross_lig, cross_rec = lr_edge_index | |
| lr_edge_attr = self.cross_edge_embedding(lr_edge_attr) | |
| for l in range(len(self.lig_conv_layers)): | |
| # intra graph message passing | |
| lig_edge_attr_ = torch.cat([lig_edge_attr, lig_node_attr[lig_src, :self.ns], lig_node_attr[lig_dst, :self.ns]], -1) | |
| lig_intra_update = self.lig_conv_layers[l](lig_node_attr, lig_edge_index, lig_edge_attr_, lig_edge_sh, edge_weight=lig_edge_weight) | |
| # inter graph message passing | |
| rec_to_lig_edge_attr_ = torch.cat([lr_edge_attr, lig_node_attr[cross_lig, :self.ns], rec_node_attr[cross_rec, :self.ns]], -1) | |
| lig_inter_update = self.rec_to_lig_conv_layers[l](rec_node_attr, lr_edge_index, rec_to_lig_edge_attr_, lr_edge_sh, | |
| out_nodes=lig_node_attr.shape[0], edge_weight=lr_edge_weight) | |
| if self.include_miscellaneous_atoms: | |
| la_edge_attr_ = torch.cat([la_edge_attr, lig_node_attr[la_edge_index[0], :self.ns],atom_node_attr[la_edge_index[1], :self.ns]], -1) | |
| la_update = self.la_conv_layers[l](atom_node_attr, la_edge_index, la_edge_attr_, la_edge_sh,out_nodes=lig_node_attr.shape[0], edge_weight=la_edge_weight) | |
| if l != len(self.lig_conv_layers) - 1: | |
| rec_edge_attr_ = torch.cat([rec_edge_attr, rec_node_attr[rec_src, :self.ns], rec_node_attr[rec_dst, :self.ns]], -1) | |
| rec_intra_update = self.rec_conv_layers[l](rec_node_attr, rec_edge_index, rec_edge_attr_, rec_edge_sh, edge_weight=rec_edge_weight) | |
| lig_to_rec_edge_attr_ = torch.cat([lr_edge_attr, lig_node_attr[cross_lig, :self.ns], rec_node_attr[cross_rec, :self.ns]], -1) | |
| rl_update = self.lig_to_rec_conv_layers[l](lig_node_attr, torch.flip(lr_edge_index, dims=[0]),lig_to_rec_edge_attr_,lr_edge_sh, out_nodes=rec_node_attr.shape[0],edge_weight=lr_edge_weight) | |
| if self.include_miscellaneous_atoms: | |
| # ATOM UPDATES | |
| atom_edge_attr_ = torch.cat([atom_edge_attr, atom_node_attr[atom_edge_index[0], :self.ns],atom_node_attr[atom_edge_index[1], :self.ns]], -1) | |
| atom_update = self.misc_conv_layers[l](atom_node_attr, atom_edge_index, atom_edge_attr_,atom_edge_sh, edge_weight=atom_edge_weight) | |
| al_edge_attr_ = torch.cat([la_edge_attr, atom_node_attr[la_edge_index[1], :self.ns],lig_node_attr[la_edge_index[0], :self.ns]], -1) | |
| al_update = self.al_conv_layers[l](lig_node_attr, torch.flip(la_edge_index, dims=[0]),al_edge_attr_,la_edge_sh, out_nodes=atom_node_attr.shape[0],edge_weight=la_edge_weight) | |
| ar_edge_attr_ = torch.cat([ar_edge_attr, atom_node_attr[ar_edge_index[0], :self.ns],rec_node_attr[ar_edge_index[1], :self.ns]], -1) | |
| ar_update = self.ar_conv_layers[l](rec_node_attr, ar_edge_index, ar_edge_attr_, ar_edge_sh,out_nodes=atom_node_attr.shape[0],edge_weight=ar_edge_weight) | |
| ra_edge_attr_ = torch.cat([ar_edge_attr, rec_node_attr[ar_edge_index[1], :self.ns],atom_node_attr[ar_edge_index[0], :self.ns]], -1) | |
| ra_update = self.ra_conv_layers[l](atom_node_attr, torch.flip(ar_edge_index, dims=[0]), ra_edge_attr_, ar_edge_sh, out_nodes=rec_node_attr.shape[0], edge_weight=ar_edge_weight) | |
| # padding original features | |
| lig_node_attr = F.pad(lig_node_attr, (0, lig_intra_update.shape[-1] - lig_node_attr.shape[-1])) | |
| # update features with residual updates | |
| lig_node_attr = lig_node_attr + lig_intra_update + lig_inter_update | |
| if self.include_miscellaneous_atoms: | |
| lig_node_attr += la_update | |
| if l != len(self.lig_conv_layers) - 1: | |
| rec_node_attr = F.pad(rec_node_attr, (0, rec_intra_update.shape[-1] - rec_node_attr.shape[-1])) | |
| rec_node_attr = rec_node_attr + rec_intra_update + rl_update | |
| if self.include_miscellaneous_atoms: | |
| rec_node_attr += ra_update | |
| atom_node_attr = F.pad(atom_node_attr, (0, atom_update.shape[-1] - atom_node_attr.shape[-1])) | |
| atom_node_attr = atom_node_attr + atom_update + al_update + ar_update | |
| # compute confidence score | |
| if self.confidence_mode: | |
| scalar_lig_attr = torch.cat([lig_node_attr[:,:self.ns],lig_node_attr[:,-self.ns:] ], dim=1) if self.num_conv_layers >= 3 else lig_node_attr[:,:self.ns] | |
| confidence = self.confidence_predictor(scatter_mean(scalar_lig_attr, data['ligand'].batch, dim=0)).squeeze(dim=-1) | |
| return confidence | |
| # compute translational and rotational score vectors | |
| center_edge_index, center_edge_attr, center_edge_sh = self.build_center_conv_graph(data) | |
| center_edge_attr = self.center_edge_embedding(center_edge_attr) | |
| if self.fixed_center_conv: | |
| center_edge_attr = torch.cat([center_edge_attr, lig_node_attr[center_edge_index[1], :self.ns]], -1) | |
| else: | |
| center_edge_attr = torch.cat([center_edge_attr, lig_node_attr[center_edge_index[0], :self.ns]], -1) | |
| global_pred = self.final_conv(lig_node_attr, center_edge_index, center_edge_attr, center_edge_sh, out_nodes=data.num_graphs) | |
| tr_pred = global_pred[:, :3] + (global_pred[:, 6:9] if not self.odd_parity else 0) | |
| rot_pred = global_pred[:, 3:6] + (global_pred[:, 9:] if not self.odd_parity else 0) | |
| if self.separate_noise_schedule: | |
| data.graph_sigma_emb = torch.cat([self.timestep_emb_func(data.complex_t[noise_type]) for noise_type in ['tr','rot','tor']], dim=1) | |
| elif self.asyncronous_noise_schedule: | |
| data.graph_sigma_emb = self.timestep_emb_func(data.complex_t['t']) | |
| else: # tr rot and tor noise is all the same in this case | |
| data.graph_sigma_emb = self.timestep_emb_func(data.complex_t['tr']) | |
| # fix the magnitude of translational and rotational score vectors | |
| tr_norm = torch.linalg.vector_norm(tr_pred, dim=1).unsqueeze(1) | |
| tr_pred = tr_pred / tr_norm * self.tr_final_layer(torch.cat([tr_norm, data.graph_sigma_emb], dim=1)) | |
| rot_norm = torch.linalg.vector_norm(rot_pred, dim=1).unsqueeze(1) | |
| rot_pred = rot_pred / rot_norm * self.rot_final_layer(torch.cat([rot_norm, data.graph_sigma_emb], dim=1)) | |
| if self.scale_by_sigma: | |
| tr_pred = tr_pred / tr_sigma.unsqueeze(1) | |
| rot_pred = rot_pred * so3.score_norm(rot_sigma.cpu()).unsqueeze(1).to(data['ligand'].x.device) | |
| if self.no_torsion or data['ligand'].edge_mask.sum() == 0: return tr_pred, rot_pred, torch.empty(0, device=self.device) | |
| # torsional components | |
| tor_bonds, tor_edge_index, tor_edge_attr, tor_edge_sh, tor_edge_weight = self.build_bond_conv_graph(data) | |
| tor_bond_vec = data['ligand'].pos[tor_bonds[1]] - data['ligand'].pos[tor_bonds[0]] | |
| tor_bond_attr = lig_node_attr[tor_bonds[0]] + lig_node_attr[tor_bonds[1]] | |
| tor_bonds_sh = o3.spherical_harmonics("2e", tor_bond_vec, normalize=True, normalization='component') | |
| tor_edge_sh = self.final_tp_tor(tor_edge_sh, tor_bonds_sh[tor_edge_index[0]]) | |
| tor_edge_attr = torch.cat([tor_edge_attr, lig_node_attr[tor_edge_index[1], :self.ns], | |
| tor_bond_attr[tor_edge_index[0], :self.ns]], -1) | |
| tor_pred = self.tor_bond_conv(lig_node_attr, tor_edge_index, tor_edge_attr, tor_edge_sh, | |
| out_nodes=data['ligand'].edge_mask.sum(), reduce='mean', edge_weight=tor_edge_weight) | |
| tor_pred = self.tor_final_layer(tor_pred).squeeze(1) | |
| edge_sigma = tor_sigma[data['ligand'].batch][data['ligand', 'ligand'].edge_index[0]][data['ligand'].edge_mask] | |
| if self.scale_by_sigma: | |
| tor_pred = tor_pred * torch.sqrt(torch.tensor(torus.score_norm(edge_sigma.cpu().numpy())).float() | |
| .to(data['ligand'].x.device)) | |
| return tr_pred, rot_pred, tor_pred | |
| def get_edge_weight(self, edge_vec, max_norm): | |
| # computes weights for edges that are decreasing with the distance | |
| # it has an effect only if smooth edges is true | |
| if self.smooth_edges: | |
| normalised_norm = torch.clip(edge_vec.norm(dim=-1) * np.pi / max_norm, max=np.pi) | |
| return 0.5 * (torch.cos(normalised_norm) + 1.0).unsqueeze(-1) | |
| return 1.0 | |
| def build_lig_conv_graph(self, data): | |
| # builds the ligand graph edges and initial node and edge features | |
| if self.separate_noise_schedule: | |
| data['ligand'].node_sigma_emb = torch.cat([self.timestep_emb_func(data['ligand'].node_t[noise_type]) for noise_type in ['tr','rot','tor']], dim=1) | |
| elif self.asyncronous_noise_schedule: | |
| data['ligand'].node_sigma_emb = self.timestep_emb_func(data['ligand'].node_t['t']) | |
| else: | |
| data['ligand'].node_sigma_emb = self.timestep_emb_func(data['ligand'].node_t['tr']) # tr rot and tor noise is all the same | |
| # compute edges | |
| radius_edges = radius_graph(data['ligand'].pos, self.lig_max_radius, data['ligand'].batch) | |
| edge_index = torch.cat([data['ligand', 'ligand'].edge_index, radius_edges], 1).long() | |
| edge_attr = torch.cat([ | |
| data['ligand', 'ligand'].edge_attr, | |
| torch.zeros(radius_edges.shape[-1], self.in_lig_edge_features, device=data['ligand'].x.device) | |
| ], 0) | |
| # compute initial features | |
| edge_sigma_emb = data['ligand'].node_sigma_emb[edge_index[0].long()] | |
| edge_attr = torch.cat([edge_attr, edge_sigma_emb], 1) | |
| node_attr = torch.cat([data['ligand'].x, data['ligand'].node_sigma_emb], 1) | |
| src, dst = edge_index | |
| edge_vec = data['ligand'].pos[dst.long()] - data['ligand'].pos[src.long()] | |
| edge_length_emb = self.lig_distance_expansion(edge_vec.norm(dim=-1)) | |
| edge_attr = torch.cat([edge_attr, edge_length_emb], 1) | |
| edge_sh = o3.spherical_harmonics(self.sh_irreps, edge_vec, normalize=True, normalization='component') | |
| edge_weight = self.get_edge_weight(edge_vec, self.lig_max_radius) | |
| return node_attr, edge_index, edge_attr, edge_sh, edge_weight | |
| def build_rec_conv_graph(self, data): | |
| # builds the receptor initial node and edge embeddings | |
| if self.separate_noise_schedule: | |
| data['receptor'].node_sigma_emb = torch.cat([self.timestep_emb_func(data['receptor'].node_t[noise_type]) for noise_type in ['tr', 'rot', 'tor']], dim=1) | |
| elif self.asyncronous_noise_schedule: | |
| data['receptor'].node_sigma_emb = self.timestep_emb_func(data['receptor'].node_t['t']) | |
| else: | |
| data['receptor'].node_sigma_emb = self.timestep_emb_func(data['receptor'].node_t['tr']) # tr rot and tor noise is all the same | |
| node_attr = torch.cat([data['receptor'].x, data['receptor'].node_sigma_emb], 1) | |
| # this assumes the edges were already created in preprocessing since protein's structure is fixed | |
| edge_index = data['receptor', 'receptor'].edge_index | |
| src, dst = edge_index | |
| edge_vec = data['receptor'].pos[dst.long()] - data['receptor'].pos[src.long()] | |
| edge_length_emb = self.rec_distance_expansion(edge_vec.norm(dim=-1)) | |
| edge_sigma_emb = data['receptor'].node_sigma_emb[edge_index[0].long()] | |
| edge_attr = torch.cat([edge_sigma_emb, edge_length_emb], 1) | |
| edge_sh = o3.spherical_harmonics(self.sh_irreps, edge_vec, normalize=True, normalization='component') | |
| edge_weight = self.get_edge_weight(edge_vec, self.rec_max_radius) | |
| return node_attr, edge_index, edge_attr, edge_sh, edge_weight | |
| def build_misc_atom_conv_graph(self, data): | |
| # build the graph between receptor misc_atoms | |
| if self.separate_noise_schedule: | |
| data['misc_atom'].node_sigma_emb = torch.cat([self.timestep_emb_func(data['misc_atom'].node_t[noise_type]) for noise_type in ['tr', 'rot', 'tor']],dim=1) | |
| elif self.asyncronous_noise_schedule: | |
| data['misc_atom'].node_sigma_emb = self.timestep_emb_func(data['misc_atom'].node_t['t']) | |
| else: | |
| data['misc_atom'].node_sigma_emb = self.timestep_emb_func(data['misc_atom'].node_t['tr']) # tr rot and tor noise is all the same | |
| node_attr = torch.cat([data['misc_atom'].x, data['misc_atom'].node_sigma_emb], 1) | |
| # this assumes the edges were already created in preprocessing since protein's structure is fixed | |
| edge_index = data['misc_atom', 'misc_atom'].edge_index | |
| src, dst = edge_index | |
| edge_vec = data['misc_atom'].pos[dst.long()] - data['misc_atom'].pos[src.long()] | |
| edge_length_emb = self.lig_distance_expansion(edge_vec.norm(dim=-1)) | |
| edge_sigma_emb = data['misc_atom'].node_sigma_emb[edge_index[0].long()] | |
| edge_attr = torch.cat([edge_sigma_emb, edge_length_emb], 1) | |
| edge_sh = o3.spherical_harmonics(self.sh_irreps, edge_vec, normalize=True, normalization='component') | |
| edge_weight = self.get_edge_weight(edge_vec, self.lig_max_radius) | |
| return node_attr, edge_index, edge_attr, edge_sh, edge_weight | |
| def build_cross_conv_graph(self, data, cross_distance_cutoff): | |
| # builds the cross edges between ligand and receptor | |
| if torch.is_tensor(cross_distance_cutoff): | |
| # different cutoff for every graph (depends on the diffusion time) | |
| edge_index = radius(data['receptor'].pos / cross_distance_cutoff[data['receptor'].batch], | |
| data['ligand'].pos / cross_distance_cutoff[data['ligand'].batch], 1, | |
| data['receptor'].batch, data['ligand'].batch, max_num_neighbors=10000) | |
| else: | |
| edge_index = radius(data['receptor'].pos, data['ligand'].pos, cross_distance_cutoff, | |
| data['receptor'].batch, data['ligand'].batch, max_num_neighbors=10000) | |
| src, dst = edge_index | |
| edge_vec = data['receptor'].pos[dst.long()] - data['ligand'].pos[src.long()] | |
| edge_length_emb = self.cross_distance_expansion(edge_vec.norm(dim=-1)) | |
| edge_sigma_emb = data['ligand'].node_sigma_emb[src.long()] | |
| edge_attr = torch.cat([edge_sigma_emb, edge_length_emb], 1) | |
| edge_sh = o3.spherical_harmonics(self.sh_irreps, edge_vec, normalize=True, normalization='component') | |
| cutoff_d = cross_distance_cutoff[data['ligand'].batch[src]].squeeze() if torch.is_tensor(cross_distance_cutoff) else cross_distance_cutoff | |
| edge_weight = self.get_edge_weight(edge_vec, cutoff_d) | |
| return edge_index, edge_attr, edge_sh, edge_weight | |
| def build_misc_cross_conv_graph(self, data, lr_cross_distance_cutoff): | |
| # build the cross edges between ligan atoms, receptor residues and receptor atoms | |
| # LIGAND to RECEPTOR | |
| if torch.is_tensor(lr_cross_distance_cutoff): | |
| # different cutoff for every graph | |
| lr_edge_index = radius(data['receptor'].pos / lr_cross_distance_cutoff[data['receptor'].batch], | |
| data['ligand'].pos / lr_cross_distance_cutoff[data['ligand'].batch], 1, | |
| data['receptor'].batch, data['ligand'].batch, max_num_neighbors=10000) | |
| else: | |
| lr_edge_index = radius(data['receptor'].pos, data['ligand'].pos, lr_cross_distance_cutoff, | |
| data['receptor'].batch, data['ligand'].batch, max_num_neighbors=10000) | |
| lr_edge_vec = data['receptor'].pos[lr_edge_index[1].long()] - data['ligand'].pos[lr_edge_index[0].long()] | |
| lr_edge_length_emb = self.cross_distance_expansion(lr_edge_vec.norm(dim=-1)) | |
| lr_edge_sigma_emb = data['ligand'].node_sigma_emb[lr_edge_index[0].long()] | |
| lr_edge_attr = torch.cat([lr_edge_sigma_emb, lr_edge_length_emb], 1) | |
| lr_edge_sh = o3.spherical_harmonics(self.sh_irreps, lr_edge_vec, normalize=True, normalization='component') | |
| cutoff_d = lr_cross_distance_cutoff[data['ligand'].batch[lr_edge_index[0]]].squeeze() \ | |
| if torch.is_tensor(lr_cross_distance_cutoff) else lr_cross_distance_cutoff | |
| lr_edge_weight = self.get_edge_weight(lr_edge_vec, cutoff_d) | |
| # LIGAND to ATOM | |
| la_edge_index = radius(data['misc_atom'].pos, data['ligand'].pos, self.lig_max_radius, | |
| data['misc_atom'].batch, data['ligand'].batch, max_num_neighbors=10000) | |
| la_edge_vec = data['misc_atom'].pos[la_edge_index[1].long()] - data['ligand'].pos[la_edge_index[0].long()] | |
| la_edge_length_emb = self.cross_distance_expansion(la_edge_vec.norm(dim=-1)) | |
| la_edge_sigma_emb = data['ligand'].node_sigma_emb[la_edge_index[0].long()] | |
| la_edge_attr = torch.cat([la_edge_sigma_emb, la_edge_length_emb], 1) | |
| la_edge_sh = o3.spherical_harmonics(self.sh_irreps, la_edge_vec, normalize=True, normalization='component') | |
| la_edge_weight = self.get_edge_weight(la_edge_vec, self.lig_max_radius) | |
| # ATOM to RECEPTOR | |
| ar_edge_index = data['misc_atom', 'receptor'].edge_index | |
| ar_edge_vec = data['receptor'].pos[ar_edge_index[1].long()] - data['misc_atom'].pos[ar_edge_index[0].long()] | |
| ar_edge_length_emb = self.rec_distance_expansion(ar_edge_vec.norm(dim=-1)) | |
| ar_edge_sigma_emb = data['misc_atom'].node_sigma_emb[ar_edge_index[0].long()] | |
| ar_edge_attr = torch.cat([ar_edge_sigma_emb, ar_edge_length_emb], 1) | |
| ar_edge_sh = o3.spherical_harmonics(self.sh_irreps, ar_edge_vec, normalize=True, normalization='component') | |
| ar_edge_weight = 1 | |
| return lr_edge_index, lr_edge_attr, lr_edge_sh, lr_edge_weight, la_edge_index, la_edge_attr, \ | |
| la_edge_sh, la_edge_weight, ar_edge_index, ar_edge_attr, ar_edge_sh, ar_edge_weight | |
| def build_center_conv_graph(self, data): | |
| # builds the filter and edges for the convolution generating translational and rotational scores | |
| edge_index = torch.cat([data['ligand'].batch.unsqueeze(0), torch.arange(len(data['ligand'].batch)).to(data['ligand'].x.device).unsqueeze(0)], dim=0) | |
| center_pos, count = torch.zeros((data.num_graphs, 3)).to(data['ligand'].x.device), torch.zeros((data.num_graphs, 3)).to(data['ligand'].x.device) | |
| center_pos.index_add_(0, index=data['ligand'].batch, source=data['ligand'].pos) | |
| center_pos = center_pos / torch.bincount(data['ligand'].batch).unsqueeze(1) | |
| edge_vec = data['ligand'].pos[edge_index[1]] - center_pos[edge_index[0]] | |
| edge_attr = self.center_distance_expansion(edge_vec.norm(dim=-1)) | |
| edge_sigma_emb = data['ligand'].node_sigma_emb[edge_index[1].long()] | |
| edge_attr = torch.cat([edge_attr, edge_sigma_emb], 1) | |
| edge_sh = o3.spherical_harmonics(self.sh_irreps, edge_vec, normalize=True, normalization='component') | |
| return edge_index, edge_attr, edge_sh | |
| def build_bond_conv_graph(self, data): | |
| # builds the graph for the convolution between the center of the rotatable bonds and the neighbouring nodes | |
| bonds = data['ligand', 'ligand'].edge_index[:, data['ligand'].edge_mask].long() | |
| bond_pos = (data['ligand'].pos[bonds[0]] + data['ligand'].pos[bonds[1]]) / 2 | |
| bond_batch = data['ligand'].batch[bonds[0]] | |
| edge_index = radius(data['ligand'].pos, bond_pos, self.lig_max_radius, batch_x=data['ligand'].batch, batch_y=bond_batch) | |
| edge_vec = data['ligand'].pos[edge_index[1]] - bond_pos[edge_index[0]] | |
| edge_attr = self.lig_distance_expansion(edge_vec.norm(dim=-1)) | |
| edge_attr = self.final_edge_embedding(edge_attr) | |
| edge_sh = o3.spherical_harmonics(self.sh_irreps, edge_vec, normalize=True, normalization='component') | |
| edge_weight = self.get_edge_weight(edge_vec, self.lig_max_radius) | |
| return bonds, edge_index, edge_attr, edge_sh, edge_weight | |