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_mean import numpy as np from models.score_model import AtomEncoder, TensorProductConvLayer, GaussianSmearing from utils import so3, torus from datasets.process_mols import lig_feature_dims, rec_residue_feature_dims, rec_atom_feature_dims 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=False, 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.num_conv_layers = num_conv_layers self.timestep_emb_func = timestep_emb_func self.confidence_mode = confidence_mode self.num_conv_layers = num_conv_layers # embedding 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.atom_node_embedding = AtomEncoder(emb_dim=ns, feature_dims=rec_atom_feature_dims, sigma_embed_dim=sigma_embed_dim) self.atom_edge_embedding = nn.Sequential(nn.Linear(sigma_embed_dim + distance_embed_dim, ns), nn.ReLU(), nn.Dropout(dropout),nn.Linear(ns, ns)) self.lr_edge_embedding = nn.Sequential(nn.Linear(sigma_embed_dim + cross_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.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' ] # convolutional layers 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, 'residual': False, 'batch_norm': batch_norm, 'dropout': dropout } for _ in range(9): # 3 intra & 6 inter per each layer conv_layers.append(TensorProductConvLayer(**parameters)) self.conv_layers = nn.ModuleList(conv_layers) # confidence and affinity prediction layers if self.confidence_mode: output_confidence_dim = num_confidence_outputs 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, output_confidence_dim) ) else: # convolution for translational and rotational scores 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.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: # convolution for torsional score 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.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 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 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_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_node_attr = self.rec_node_embedding(rec_node_attr) rec_edge_attr = self.rec_edge_embedding(rec_edge_attr) # build atom graph atom_node_attr, atom_edge_index, atom_edge_attr, atom_edge_sh = self.build_atom_conv_graph(data) atom_node_attr = self.atom_node_embedding(atom_node_attr) atom_edge_attr = self.atom_edge_embedding(atom_edge_attr) # build cross graph cross_cutoff = (tr_sigma * 3 + 20).unsqueeze(1) if self.dynamic_max_cross else self.cross_max_distance lr_edge_index, lr_edge_attr, lr_edge_sh, la_edge_index, la_edge_attr, \ la_edge_sh, ar_edge_index, ar_edge_attr, ar_edge_sh = self.build_cross_conv_graph(data, cross_cutoff) lr_edge_attr= self.lr_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) for l in range(self.num_conv_layers): # LIGAND updates lig_edge_attr_ = torch.cat([lig_edge_attr, lig_node_attr[lig_edge_index[0], :self.ns], lig_node_attr[lig_edge_index[1], :self.ns]], -1) lig_update = self.conv_layers[9*l](lig_node_attr, lig_edge_index, lig_edge_attr_, lig_edge_sh) lr_edge_attr_ = torch.cat([lr_edge_attr, lig_node_attr[lr_edge_index[0], :self.ns], rec_node_attr[lr_edge_index[1], :self.ns]], -1) lr_update = self.conv_layers[9*l+1](rec_node_attr, lr_edge_index, lr_edge_attr_, lr_edge_sh, out_nodes=lig_node_attr.shape[0]) 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.conv_layers[9*l+2](atom_node_attr, la_edge_index, la_edge_attr_, la_edge_sh, out_nodes=lig_node_attr.shape[0]) if l != self.num_conv_layers-1: # last layer optimisation # 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.conv_layers[9*l+3](atom_node_attr, atom_edge_index, atom_edge_attr_, atom_edge_sh) 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.conv_layers[9*l+4](lig_node_attr, torch.flip(la_edge_index, dims=[0]), al_edge_attr_, la_edge_sh, out_nodes=atom_node_attr.shape[0]) 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.conv_layers[9*l+5](rec_node_attr, ar_edge_index, ar_edge_attr_, ar_edge_sh, out_nodes=atom_node_attr.shape[0]) # RECEPTOR updates rec_edge_attr_ = torch.cat([rec_edge_attr, rec_node_attr[rec_edge_index[0], :self.ns], rec_node_attr[rec_edge_index[1], :self.ns]], -1) rec_update = self.conv_layers[9*l+6](rec_node_attr, rec_edge_index, rec_edge_attr_, rec_edge_sh) rl_edge_attr_ = torch.cat([lr_edge_attr, rec_node_attr[lr_edge_index[1], :self.ns], lig_node_attr[lr_edge_index[0], :self.ns]], -1) rl_update = self.conv_layers[9*l+7](lig_node_attr, torch.flip(lr_edge_index, dims=[0]), rl_edge_attr_, lr_edge_sh, out_nodes=rec_node_attr.shape[0]) 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.conv_layers[9*l+8](atom_node_attr, torch.flip(ar_edge_index, dims=[0]), ra_edge_attr_, ar_edge_sh, out_nodes=rec_node_attr.shape[0]) # padding original features and update features with residual updates lig_node_attr = F.pad(lig_node_attr, (0, lig_update.shape[-1] - lig_node_attr.shape[-1])) lig_node_attr = lig_node_attr + lig_update + la_update + lr_update if l != self.num_conv_layers - 1: # last layer optimisation atom_node_attr = F.pad(atom_node_attr, (0, atom_update.shape[-1] - rec_node_attr.shape[-1])) atom_node_attr = atom_node_attr + atom_update + al_update + ar_update rec_node_attr = F.pad(rec_node_attr, (0, rec_update.shape[-1] - rec_node_attr.shape[-1])) rec_node_attr = rec_node_attr + rec_update + ra_update + rl_update # confidence and affinity prediction 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']) # adjust the magniture of the 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): # build the graph between ligand atoms data['ligand'].node_sigma_emb = self.timestep_emb_func(data['ligand'].node_t['tr']) 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) 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): # build the graph between receptor residues data['receptor'].node_sigma_emb = self.timestep_emb_func(data['receptor'].node_t['tr']) 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_atom_conv_graph(self, data): # build the graph between receptor atoms data['atom'].node_sigma_emb = self.timestep_emb_func(data['atom'].node_t['tr']) node_attr = torch.cat([data['atom'].x, data['atom'].node_sigma_emb], 1) # this assumes the edges were already created in preprocessing since protein's structure is fixed edge_index = data['atom', 'atom'].edge_index src, dst = edge_index edge_vec = data['atom'].pos[dst.long()] - data['atom'].pos[src.long()] edge_length_emb = self.lig_distance_expansion(edge_vec.norm(dim=-1)) edge_sigma_emb = data['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') return node_attr, edge_index, edge_attr, edge_sh def build_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 # LIGAND to ATOM la_edge_index = radius(data['atom'].pos, data['ligand'].pos, self.lig_max_radius, data['atom'].batch, data['ligand'].batch, max_num_neighbors=10000) la_edge_vec = data['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') # ATOM to RECEPTOR ar_edge_index = data['atom', 'receptor'].edge_index ar_edge_vec = data['receptor'].pos[ar_edge_index[1].long()] - data['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['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') return lr_edge_index, lr_edge_attr, lr_edge_sh, la_edge_index, la_edge_attr, \ la_edge_sh, ar_edge_index, ar_edge_attr, ar_edge_sh def build_center_conv_graph(self, data): # build the filter for the convolution of the center with the ligand atoms # for translational and rotational score 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): # build graph for the pseudotorque layer 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