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 utils import so3, torus from datasets.process_mols import lig_feature_dims, rec_residue_feature_dims class AtomEncoder(torch.nn.Module): def __init__(self, emb_dim, feature_dims, sigma_embed_dim, lm_embedding_type= None): # first element of feature_dims tuple is a list with the lenght of each categorical feature and the second is the number of scalar features super(AtomEncoder, self).__init__() self.atom_embedding_list = torch.nn.ModuleList() self.num_categorical_features = len(feature_dims[0]) self.num_scalar_features = feature_dims[1] + sigma_embed_dim self.lm_embedding_type = lm_embedding_type for i, dim in enumerate(feature_dims[0]): emb = torch.nn.Embedding(dim, emb_dim) torch.nn.init.xavier_uniform_(emb.weight.data) self.atom_embedding_list.append(emb) if self.num_scalar_features > 0: self.linear = torch.nn.Linear(self.num_scalar_features, emb_dim) if self.lm_embedding_type is not None: if self.lm_embedding_type == 'esm': self.lm_embedding_dim = 1280 else: raise ValueError('LM Embedding type was not correctly determined. LM embedding type: ', self.lm_embedding_type) self.lm_embedding_layer = torch.nn.Linear(self.lm_embedding_dim + emb_dim, emb_dim) def forward(self, x): x_embedding = 0 if self.lm_embedding_type is not None: assert x.shape[1] == self.num_categorical_features + self.num_scalar_features + self.lm_embedding_dim else: assert x.shape[1] == self.num_categorical_features + self.num_scalar_features for i in range(self.num_categorical_features): x_embedding += self.atom_embedding_list[i](x[:, i].long()) if self.num_scalar_features > 0: x_embedding += self.linear(x[:, self.num_categorical_features:self.num_categorical_features + self.num_scalar_features]) if self.lm_embedding_type is not None: x_embedding = self.lm_embedding_layer(torch.cat([x_embedding, x[:, -self.lm_embedding_dim:]], axis=1)) return x_embedding class TensorProductConvLayer(torch.nn.Module): def __init__(self, in_irreps, sh_irreps, out_irreps, n_edge_features, residual=True, batch_norm=True, dropout=0.0, hidden_features=None): super(TensorProductConvLayer, self).__init__() self.in_irreps = in_irreps self.out_irreps = out_irreps self.sh_irreps = sh_irreps self.residual = residual if hidden_features is None: hidden_features = n_edge_features self.tp = tp = o3.FullyConnectedTensorProduct(in_irreps, sh_irreps, out_irreps, shared_weights=False) self.fc = nn.Sequential( nn.Linear(n_edge_features, hidden_features), nn.ReLU(), nn.Dropout(dropout), nn.Linear(hidden_features, tp.weight_numel) ) self.batch_norm = BatchNorm(out_irreps) if batch_norm else None def forward(self, node_attr, edge_index, edge_attr, edge_sh, out_nodes=None, reduce='mean'): edge_src, edge_dst = edge_index tp = self.tp(node_attr[edge_dst], edge_sh, self.fc(edge_attr)) out_nodes = out_nodes or node_attr.shape[0] out = scatter(tp, edge_src, dim=0, dim_size=out_nodes, reduce=reduce) if self.residual: padded = F.pad(node_attr, (0, out.shape[-1] - node_attr.shape[-1])) out = out + padded if self.batch_norm: out = self.batch_norm(out) return out class TensorProductScoreModel(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, use_second_order_repr=False, batch_norm=True, dynamic_max_cross=False, dropout=0.0, lm_embedding_type=None, confidence_mode=False, confidence_dropout=0, confidence_no_batchnorm=False, num_confidence_outputs=1): super(TensorProductScoreModel, self).__init__() self.t_to_sigma = t_to_sigma self.in_lig_edge_features = in_lig_edge_features self.sigma_embed_dim = sigma_embed_dim self.lig_max_radius = lig_max_radius self.rec_max_radius = rec_max_radius 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.device = device self.no_torsion = no_torsion self.timestep_emb_func = timestep_emb_func self.confidence_mode = confidence_mode self.num_conv_layers = num_conv_layers self.lig_node_embedding = AtomEncoder(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 = AtomEncoder(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)) 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 = [], [], [], [] 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 = TensorProductConvLayer(**parameters) lig_conv_layers.append(lig_layer) rec_layer = TensorProductConvLayer(**parameters) rec_conv_layers.append(rec_layer) lig_to_rec_layer = TensorProductConvLayer(**parameters) lig_to_rec_conv_layers.append(lig_to_rec_layer) rec_to_lig_layer = TensorProductConvLayer(**parameters) rec_to_lig_conv_layers.append(rec_to_lig_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.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, num_confidence_outputs) ) 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 = TensorProductConvLayer( in_irreps=self.lig_conv_layers[-1].out_irreps, sh_irreps=self.sh_irreps, out_irreps=f'2x1o + 2x1e', 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 = TensorProductConvLayer( in_irreps=self.lig_conv_layers[-1].out_irreps, sh_irreps=self.final_tp_tor.irreps_out, out_irreps=f'{ns}x0o + {ns}x0e', n_edge_features=3 * ns, residual=False, dropout=dropout, batch_norm=batch_norm ) self.tor_final_layer = nn.Sequential( nn.Linear(2 * ns, ns, bias=False), nn.Tanh(), nn.Dropout(dropout), nn.Linear(ns, 1, bias=False) ) def forward(self, data): 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 = 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 = 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) # build cross graph if self.dynamic_max_cross: cross_cutoff = (tr_sigma * 3 + 20).unsqueeze(1) else: cross_cutoff = self.cross_max_distance cross_edge_index, cross_edge_attr, cross_edge_sh = self.build_cross_conv_graph(data, cross_cutoff) cross_lig, cross_rec = cross_edge_index cross_edge_attr = self.cross_edge_embedding(cross_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) # inter graph message passing rec_to_lig_edge_attr_ = torch.cat([cross_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, cross_edge_index, rec_to_lig_edge_attr_, cross_edge_sh, out_nodes=lig_node_attr.shape[0]) 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) lig_to_rec_edge_attr_ = torch.cat([cross_edge_attr, lig_node_attr[cross_lig, :self.ns], rec_node_attr[cross_rec, :self.ns]], -1) rec_inter_update = self.lig_to_rec_conv_layers[l](lig_node_attr, torch.flip(cross_edge_index, dims=[0]), lig_to_rec_edge_attr_, cross_edge_sh, out_nodes=rec_node_attr.shape[0]) # 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 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 + rec_inter_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) 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] rot_pred = global_pred[:, 3:6] + global_pred[:, 9:] 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 = 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') 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 build_lig_conv_graph(self, data): # builds the ligand graph edges and initial node and edge features data['ligand'].node_sigma_emb = self.timestep_emb_func(data['ligand'].node_t['tr']) # 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') return node_attr, edge_index, edge_attr, edge_sh def build_rec_conv_graph(self, data): # builds the receptor initial node and edge embeddings 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') return node_attr, edge_index, edge_attr, edge_sh 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') return edge_index, edge_attr, edge_sh 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') return bonds, edge_index, edge_attr, edge_sh class GaussianSmearing(torch.nn.Module): # used to embed the edge distances def __init__(self, start=0.0, stop=5.0, num_gaussians=50): super().__init__() offset = torch.linspace(start, stop, num_gaussians) self.coeff = -0.5 / (offset[1] - offset[0]).item() ** 2 self.register_buffer('offset', offset) def forward(self, dist): dist = dist.view(-1, 1) - self.offset.view(1, -1) return torch.exp(self.coeff * torch.pow(dist, 2))