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| import networkx as nx | |
| import numpy as np | |
| import torch, copy | |
| from scipy.spatial.transform import Rotation as R | |
| from torch_geometric.utils import to_networkx | |
| from torch_geometric.data import Data | |
| """ | |
| Preprocessing and computation for torsional updates to conformers | |
| """ | |
| def get_transformation_mask(pyg_data): | |
| G = to_networkx(pyg_data.to_homogeneous(), to_undirected=False) | |
| to_rotate = [] | |
| edges = pyg_data['ligand', 'ligand'].edge_index.T.numpy() | |
| for i in range(0, edges.shape[0], 2): | |
| assert edges[i, 0] == edges[i+1, 1] | |
| G2 = G.to_undirected() | |
| G2.remove_edge(*edges[i]) | |
| if not nx.is_connected(G2): | |
| l = list(sorted(nx.connected_components(G2), key=len)[0]) | |
| if len(l) > 1: | |
| if edges[i, 0] in l: | |
| to_rotate.append([]) | |
| to_rotate.append(l) | |
| else: | |
| to_rotate.append(l) | |
| to_rotate.append([]) | |
| continue | |
| to_rotate.append([]) | |
| to_rotate.append([]) | |
| mask_edges = np.asarray([0 if len(l) == 0 else 1 for l in to_rotate], dtype=bool) | |
| mask_rotate = np.zeros((np.sum(mask_edges), len(G.nodes())), dtype=bool) | |
| idx = 0 | |
| for i in range(len(G.edges())): | |
| if mask_edges[i]: | |
| mask_rotate[idx][np.asarray(to_rotate[i], dtype=int)] = True | |
| idx += 1 | |
| return mask_edges, mask_rotate | |
| def modify_conformer_torsion_angles(pos, edge_index, mask_rotate, torsion_updates, as_numpy=False): | |
| pos = copy.deepcopy(pos) | |
| if type(pos) != np.ndarray: pos = pos.cpu().numpy() | |
| for idx_edge, e in enumerate(edge_index.cpu().numpy()): | |
| if torsion_updates[idx_edge] == 0: | |
| continue | |
| u, v = e[0], e[1] | |
| # check if need to reverse the edge, v should be connected to the part that gets rotated | |
| assert not mask_rotate[idx_edge, u] | |
| assert mask_rotate[idx_edge, v] | |
| rot_vec = pos[u] - pos[v] # convention: positive rotation if pointing inwards | |
| rot_vec = rot_vec * torsion_updates[idx_edge] / np.linalg.norm(rot_vec) # idx_edge! | |
| rot_mat = R.from_rotvec(rot_vec).as_matrix() | |
| pos[mask_rotate[idx_edge]] = (pos[mask_rotate[idx_edge]] - pos[v]) @ rot_mat.T + pos[v] | |
| if not as_numpy: pos = torch.from_numpy(pos.astype(np.float32)) | |
| return pos | |
| def perturb_batch(data, torsion_updates, split=False, return_updates=False): | |
| if type(data) is Data: | |
| return modify_conformer_torsion_angles(data.pos, | |
| data.edge_index.T[data.edge_mask], | |
| data.mask_rotate, torsion_updates) | |
| pos_new = [] if split else copy.deepcopy(data.pos) | |
| edges_of_interest = data.edge_index.T[data.edge_mask] | |
| idx_node = 0 | |
| idx_edges = 0 | |
| torsion_update_list = [] | |
| for i, mask_rotate in enumerate(data.mask_rotate): | |
| pos = data.pos[idx_node:idx_node + mask_rotate.shape[1]] | |
| edges = edges_of_interest[idx_edges:idx_edges + mask_rotate.shape[0]] - idx_node | |
| torsion_update = torsion_updates[idx_edges:idx_edges + mask_rotate.shape[0]] | |
| torsion_update_list.append(torsion_update) | |
| pos_new_ = modify_conformer_torsion_angles(pos, edges, mask_rotate, torsion_update) | |
| if split: | |
| pos_new.append(pos_new_) | |
| else: | |
| pos_new[idx_node:idx_node + mask_rotate.shape[1]] = pos_new_ | |
| idx_node += mask_rotate.shape[1] | |
| idx_edges += mask_rotate.shape[0] | |
| if return_updates: | |
| return pos_new, torsion_update_list | |
| return pos_new |