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 from utils.geometry import rigid_transform_Kabsch_independent_torch, axis_angle_to_matrix """ 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(min(edges.shape[0], 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() if type(mask_rotate) == list: mask_rotate = mask_rotate[0] 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 if mask_rotate[idx_edge, u] or (not mask_rotate[idx_edge, v]): print("mask rotate exception") #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 modify_conformer_torsion_angles_batch(pos, edge_index, mask_rotate, torsion_updates): pos = pos + 0 for idx_edge, e in enumerate(edge_index): 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_mat = axis_angle_to_matrix( rot_vec / torch.linalg.norm(rot_vec, dim=-1, keepdims=True) * torsion_updates[:, idx_edge:idx_edge + 1]) pos[:, mask_rotate[idx_edge]] = torch.bmm(pos[:, mask_rotate[idx_edge]] - pos[:, v:v + 1], torch.transpose(rot_mat, 1, 2)) + pos[:, v:v + 1] 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 def get_dihedrals(data_list): edge_index, edge_mask = data_list[0]['ligand', 'ligand'].edge_index, data_list[0]['ligand'].edge_mask edge_list = [[] for _ in range(torch.max(edge_index) + 1)] for p in edge_index.T: edge_list[p[0]].append(p[1]) rot_bonds = [(p[0], p[1]) for i, p in enumerate(edge_index.T) if edge_mask[i]] dihedral = [] for a, b in rot_bonds: c = edge_list[a][0] if edge_list[a][0] != b else edge_list[a][1] d = edge_list[b][0] if edge_list[b][0] != a else edge_list[b][1] dihedral.append((c.item(), a.item(), b.item(), d.item())) # dihedral_numpy = np.asarray(dihedral) # print(dihedral_numpy.shape) dihedral = torch.tensor(dihedral) return dihedral