├── LICENSE ├── README.md ├── __init__.py ├── data ├── __init__.py ├── download_data.py ├── qm9_config.txt ├── qm9_kekulized_ggnp.txt ├── transform_qm9.py ├── transform_zinc250k.py ├── valid_idx_qm9.json ├── valid_idx_zinc.json ├── zinc250k_config.txt └── zinc250k_kekulized_ggnp.txt ├── framework.png ├── graph_nvp ├── __init__.py ├── coupling.py ├── hyperparams.py ├── mlp.py ├── nvp_model.py └── rgcn.py ├── train.sh ├── train_mle.py └── utils ├── __init__.py ├── argparser.py ├── data_utils.py ├── environment.py ├── fpscores.pkl.gz ├── molecular_metrics.py └── sascorer.py /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Hanlin Zhang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # GraphNVP: An Invertible Flow Model for Generating Molecular Graphs 2 | 3 | Implementation of GraphNVP(https://arxiv.org/abs/1905.11600) using PyTorch. 4 | 5 |

6 | 7 |

8 | 9 | ## Data Preparation 10 | `python ./data/download_data.py --data_name=qm9` 11 | 12 | `python ./data/download_data.py --data_name=zinc250k` 13 | 14 | ## Train GraphNVP 15 | `bash ./train.sh` 16 | 17 | ## Citation 18 | ``` 19 | @misc{madhawa2019graphnvp, 20 | title={GraphNVP: An Invertible Flow Model for Generating Molecular Graphs}, 21 | author={Kaushalya Madhawa and Katushiko Ishiguro and Kosuke Nakago and Motoki Abe}, 22 | year={2019}, 23 | eprint={1905.11600}, 24 | archivePrefix={arXiv}, 25 | primaryClass={stat.ML} 26 | } 27 | ``` 28 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hlzhang109/PyTorch-GraphNVP/eb6cabd1e5e39f84ffe92662ef5d667aa4e30db3/__init__.py -------------------------------------------------------------------------------- /data/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hlzhang109/PyTorch-GraphNVP/eb6cabd1e5e39f84ffe92662ef5d667aa4e30db3/data/__init__.py -------------------------------------------------------------------------------- /data/download_data.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | 4 | from chainer_chemistry import datasets 5 | from chainer_chemistry.datasets import NumpyTupleDataset 6 | from chainer_chemistry.dataset.preprocessors import RSGCNPreprocessor, GGNNPreprocessor 7 | 8 | ''' 9 | From https://github.com/pfnet-research/graph-nvp/tree/master/data 10 | ''' 11 | 12 | def parse(): 13 | parser = argparse.ArgumentParser(description='') 14 | parser.add_argument('--data_name', type=str, default='qm9', 15 | choices=['qm9', 'zinc250k'], 16 | help='dataset to be downloaded') 17 | parser.add_argument('--data_type', type=str, default='relgcn', 18 | choices=['gcn', 'relgcn'],) 19 | parser.add_argument('--data_dir', type=str, default='./data') 20 | args = parser.parse_args() 21 | return args 22 | 23 | def to_smiles_txt(smiles, path): 24 | with open(path, 'w') as f: 25 | for smile in smiles: 26 | f.write(smile + '\n') 27 | 28 | args = parse() 29 | data_name = args.data_name 30 | data_type = args.data_type 31 | print('args', vars(args)) 32 | 33 | if data_name == 'qm9': 34 | max_atoms = 9 35 | elif data_name == 'zinc250k': 36 | max_atoms = 38 37 | else: 38 | raise ValueError("[ERROR] Unexpected value data_name={}".format(data_name)) 39 | 40 | if data_type == 'gcn': 41 | preprocessor = RSGCNPreprocessor(out_size=max_atoms) 42 | elif data_type == 'relgcn': 43 | # preprocessor = GGNNPreprocessor(out_size=max_atoms, kekulize=True, return_is_real_node=False) 44 | preprocessor = GGNNPreprocessor(out_size=max_atoms, kekulize=True) 45 | else: 46 | raise ValueError("[ERROR] Unexpected value data_type={}".format(data_type)) 47 | 48 | #data_dir = "." 49 | #os.makedirs(data_dir, exist_ok=True) 50 | 51 | if data_name == 'qm9': 52 | dataset, smiles = datasets.get_qm9(preprocessor, return_smiles=True) 53 | elif data_name == 'zinc250k': 54 | dataset, smiles = datasets.get_zinc250k(preprocessor, return_smiles=True) 55 | elif data_name == 'tox21': 56 | dataset, smiles = datasets.get_tox21(preprocessor, return_smiles=True) 57 | else: 58 | raise ValueError("[ERROR] Unexpected value data_name={}".format(data_name)) 59 | 60 | to_smiles_txt(smiles, os.path.join(args.data_dir, '{}_kekulized_ggnp.txt'.format(data_name))) 61 | NumpyTupleDataset.save(os.path.join(args.data_dir, '{}_{}_kekulized_ggnp.npz'.format(data_name, data_type)), dataset) 62 | -------------------------------------------------------------------------------- /data/qm9_config.txt: -------------------------------------------------------------------------------- 1 | {'atom_list': [6, 7, 8, 9, 0], 'freedom': 0, 'node_dim': 5, 'max_size': 9, 'bond_dim': 4} -------------------------------------------------------------------------------- /data/transform_qm9.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import json 3 | 4 | def one_hot(data, out_size=9, num_max_id=5): 5 | assert data.shape[0] == out_size 6 | b = np.zeros((out_size, num_max_id)) 7 | # data = data[data > 0] 8 | # 6 is C: Carbon, we adopt 6:C, 7:N, 8:O, 9:F only. the last place (4) is for padding virtual node. 9 | indices = np.where(data >= 6, data - 6, num_max_id - 1) 10 | b[np.arange(out_size), indices] = 1 11 | # print('[DEBUG] data', data, 'b', b) 12 | return b 13 | 14 | def transform_fn(data): 15 | node, adj, label = data 16 | # convert to one-hot vector 17 | node = one_hot(node).astype(np.float32) 18 | # single, double, triple and no-bond. Note that last channel axis is not connected instead of aromatic bond. 19 | adj = np.concatenate([adj[:3], 1 - np.sum(adj[:3], axis=0, keepdims=True)], 20 | axis=0).astype(np.float32) 21 | return node, adj, label 22 | 23 | 24 | def get_val_ids(): 25 | file_path = 'data/valid_idx_qm9.json' 26 | print('loading train/valid split information from: {}'.format(file_path)) 27 | with open(file_path) as json_data: 28 | data = json.load(json_data) 29 | val_ids = [int(idx)-1 for idx in data['valid_idxs']] 30 | return val_ids -------------------------------------------------------------------------------- /data/transform_zinc250k.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | import numpy as np 4 | 5 | zinc250_atomic_num_list = [6, 7, 8, 9, 15, 16, 17, 35, 53, 0] # 0 is for virtual node. 6 | max_atoms = 38 7 | n_bonds = 4 8 | 9 | 10 | def one_hot_zinc250k(data, out_size=38): 11 | num_max_id = len(zinc250_atomic_num_list) 12 | assert data.shape[0] == out_size 13 | b = np.zeros((out_size, num_max_id), dtype=np.float32) 14 | for i in range(out_size): 15 | ind = zinc250_atomic_num_list.index(data[i]) 16 | b[i, ind] = 1. 17 | return b 18 | 19 | 20 | def transform_fn_zinc250k(data): 21 | node, adj, label = data 22 | # convert to one-hot vector 23 | # node = one_hot(node).astype(np.float32) 24 | node = one_hot_zinc250k(node).astype(np.float32) 25 | # single, double, triple and no-bond. Note that last channel axis is not connected instead of aromatic bond. 26 | adj = np.concatenate([adj[:3], 1 - np.sum(adj[:3], axis=0, keepdims=True)], 27 | axis=0).astype(np.float32) 28 | return node, adj, label 29 | 30 | 31 | def get_val_ids(): 32 | file_path = 'data/valid_idx_zinc.json' 33 | print('loading train/valid split information from: {}'.format(file_path)) 34 | with open(file_path) as json_data: 35 | data = json.load(json_data) 36 | val_ids = [idx-1 for idx in data] 37 | return val_ids 38 | -------------------------------------------------------------------------------- /data/valid_idx_qm9.json: -------------------------------------------------------------------------------- 1 | { "valid_idxs" : [ "007278", "010763", "102219", "052128", "008481", "001924", "105861", "041205", "073853", "118848", "069978", "107256", "119481", "050813", "005396", "025752", "049466", "113387", "038252", "005318", "007882", "130286", "043831", "119218", "077108", "093782", "116420", "082654", 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"051979", "085274", "033035", "094308", "007535", "128313", "031314", "095045", "046216", "001520", "019152", "053702", "018345", "070924", "084147", "133158", "119057", "103612", "103381", "027640", "077436", "105175", "103760", "024393", "005862", "096942", "007466", "030861", "122157", "095500", "014617", "029407", "022015", "083353", "055432", "053669", "014620", "087454", "004536", "042934", "036427", "004633", "080120", "105728", "123046", "028058", "013985", "076761", "044287", "071697", "020784", "093963", "060718", "121655", "115322", "098512", "093306", "132894", "044024", "067859", "120857", "073012", "125950", "008239", "072759", "111305", "079473", "088564", "019950", "065855", "049156", "065657", "113511", "099604", "005363", "013234", "045015", "112325", "084539", "085811", "059374", "102926", "032344", "122850", "110059", "116060", "040654", "104622", "108271", "092203", "097284", "090943", "047091", "110750", "052479", "010741", "000945", "099259", "088719", "022785", "052682", "008585", "011947", "000442", "034779", "122442", "009423", "004724", "043849", "035191", "131753", "070326", "081630", "049344", "091053", "092332", "031108", "001153", "098570", "014453", "081769", "085294", "005884", "091607", "030814", "085090", "122273", "043576", "086465", "060744", "023845", "107231", "045689", "048684", "121684", "033706", "081698", "111950", "095478", "017754", "124020", "079926", "002701", "049198", "122690", "120043", "022775", "022872", "070441", "043720", "107312", "027028", "103877", "003532", "016313", "043476", "119268", "101188", "049451", "033814", "024034", "117628", "008909", "030460", "117102", "095394", "103184", "030844", "113509", "012611", "119616", "011367", "046708", "097466", "048611", "129544", "081074", "078116", "075460", "098306", "083519", "053132", "009135", "128772", "091112", "016781", "089166", "017599", "107275", "098207", "107344", "051947", "007828", "062830", "089104", "079133", "123302", "114938", "121852", "022794", "024378", "067154", "113433", "014495", "072271", "109742", "041433", "076625", "019731", "127372", "114908", "121267", "089688", "054806", "050426", "119739", "055250", "034020", "066592", "015583", "066595", "075100", "081121", "058016", "012336", "063821", "003397", "043552", "029335", "133239", "124195", "109614", "031905", "097619", "101086", "089474", "024618", "085286", "036482", "088767", "131150", "048273", "087163", "014741", "083014", "122301", "133485", "103403", "027504", "084478", "053461", "065120", "070751", "067165" ]} -------------------------------------------------------------------------------- /data/zinc250k_config.txt: -------------------------------------------------------------------------------- 1 | {'atom_list': [6, 7, 8, 9, 15, 16, 17, 35, 53, 0], 'freedom': 0, 'node_dim': 10, 'max_size': 38, 'bond_dim': 4} -------------------------------------------------------------------------------- /framework.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hlzhang109/PyTorch-GraphNVP/eb6cabd1e5e39f84ffe92662ef5d667aa4e30db3/framework.png -------------------------------------------------------------------------------- /graph_nvp/__init__.py: -------------------------------------------------------------------------------- 1 | #from . import nvp_model -------------------------------------------------------------------------------- /graph_nvp/coupling.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from graph_nvp.mlp import MLP 4 | from graph_nvp.rgcn import RGCN 5 | from utils.argparser import args 6 | 7 | def create_inv_masks(masks): 8 | inversed_masks = masks.clone() 9 | inversed_masks[inversed_masks > 0] = 2 10 | inversed_masks[inversed_masks == 0] = 1 11 | inversed_masks[inversed_masks == 2] = 0 12 | return inversed_masks 13 | 14 | class Rescale(nn.Module): 15 | def __init__(self): 16 | super(Rescale, self).__init__() 17 | self.weight = nn.Parameter(torch.zeros([1])) 18 | 19 | def forward(self, x): 20 | if torch.isnan(torch.exp(self.weight)).any(): 21 | print(self.weight) 22 | raise RuntimeError('Rescale factor has NaN entries') 23 | 24 | x = self.weight.exp() * x 25 | return x 26 | 27 | class Coupling(nn.Module): 28 | 29 | def __init__(self, num_nodes, num_relations, num_features, mask, 30 | batch_norm=False): 31 | super(Coupling, self).__init__() 32 | self.num_nodes = num_nodes 33 | self.num_relations = num_relations 34 | self.num_bonds = num_relations 35 | self.num_features = num_features 36 | 37 | self.adj_size = self.num_nodes * self.num_nodes * self.num_relations 38 | self.x_size = self.num_nodes * self.num_features 39 | self.apply_batch_norm = batch_norm 40 | self.mask = mask.to(args.device) 41 | self.inversed_mask = create_inv_masks(self.mask).to(args.device) 42 | 43 | def forward(self, *args, **kwargs): 44 | raise NotImplementedError 45 | 46 | def reverse(self): 47 | raise NotImplementedError 48 | 49 | 50 | class AffineAdjCoupling(Coupling): 51 | 52 | def __init__(self, num_nodes, num_relations, num_features, mask, 53 | batch_norm=False, num_masked_cols=1, 54 | ch_list=None): 55 | super(AffineAdjCoupling, self).__init__(num_nodes, num_relations, num_features, mask, 56 | batch_norm=batch_norm) 57 | self.num_masked_cols = num_masked_cols 58 | self.ch_list = ch_list 59 | self.adj_size = num_nodes * num_nodes * num_relations 60 | self.out_size = num_nodes * num_relations 61 | self.in_size = self.adj_size - self.out_size 62 | 63 | self.mlp = MLP(ch_list, in_size=self.in_size) 64 | self.lin = nn.Linear(ch_list[-1], 2 * self.out_size) 65 | self.scale_factor = torch.zeros(1, device=args.device) 66 | self.batch_norm = nn.BatchNorm1d(self.in_size) 67 | self.tanh = nn.Tanh() 68 | self.sigmoid = nn.Sigmoid() 69 | self.rescale = Rescale() 70 | 71 | def forward(self, adj): 72 | masked_adj = adj[:, :, self.mask>0].to(args.device) 73 | log_s, t = self._s_t_functions(masked_adj) 74 | t = t.expand(adj.shape) 75 | s = self.sigmoid(log_s + 2) 76 | s = s.expand(adj.shape) 77 | log_det_jacobian = torch.sum(torch.log(torch.abs(s)), axis=(1, 2, 3)) 78 | return adj, log_det_jacobian 79 | 80 | def reverse(self, adj): 81 | masked_adj = adj[:, :, self.mask>0].to(args.device) 82 | log_s, t = self._s_t_functions(masked_adj) 83 | t = t.expand(adj.shape) 84 | s = self.sigmoid(log_s + 2) 85 | s = s.expand(adj.shape) 86 | adj = adj * self.mask + (((adj - t)/s) * self.inversed_mask) 87 | return adj, None 88 | 89 | def _s_t_functions(self, adj): 90 | x = adj.view(adj.shape[0], -1).to(args.device) 91 | if self.apply_batch_norm: 92 | x = self.batch_norm(x) 93 | y = self.mlp(x) 94 | y = self.tanh(y) 95 | y = self.lin(y) 96 | y = self.rescale(y) 97 | s = y[:, :self.out_size] 98 | t = y[:, self.out_size:] 99 | s = s.view(y.shape[0], self.num_relations, self.num_nodes, 1).to(args.device) 100 | t = t.view(y.shape[0], self.num_relations, self.num_nodes, 1).to(args.device) 101 | return s, t 102 | 103 | class AffineNodeFeatureCoupling(Coupling): 104 | 105 | def __init__(self, num_nodes, num_bonds, num_features, mask, 106 | batch_norm=False, input_type='float', 107 | num_masked_cols=1, ch_list=None): 108 | super(AffineNodeFeatureCoupling, self).__init__(num_nodes, num_bonds, 109 | num_features, mask, batch_norm=batch_norm) 110 | self.num_masked_cols = num_masked_cols 111 | self.out_size = num_features * num_masked_cols 112 | self.rgcn = RGCN(num_features, nhid=128, nout=ch_list['hidden'][0], edge_dim=self.num_bonds, 113 | num_layers=args.num_gcn_layer, dropout=0., normalization=False).to(args.device) 114 | self.lin1 = nn.Linear(ch_list['hidden'][0], out_features=ch_list['hidden'][1]) 115 | self.lin2 = nn.Linear(ch_list['hidden'][1], out_features=2*self.out_size) 116 | self.scale_factor = torch.zeros(1, device=args.device) 117 | self.batch_norm = nn.BatchNorm1d(ch_list['hidden'][0]) 118 | self.tanh = nn.Tanh() 119 | self.sigmoid = nn.Sigmoid() 120 | self.rescale = Rescale() 121 | 122 | def forward(self, x, adj): 123 | masked_x = x * self.mask 124 | s, t = self._s_t_functions(masked_x, adj) 125 | x = masked_x + x * (s * self.inversed_mask) + t * self.inversed_mask 126 | log_det_jacobian = torch.sum(torch.log(torch.abs(s)), axis=(1, 2)) 127 | return x, log_det_jacobian 128 | 129 | def reverse(self, y, adj): 130 | masked_y = y * self.mask 131 | s, t = self._s_t_functions(masked_y, adj) 132 | x = masked_y + (((y - t)/s) * self.inversed_mask) 133 | return x, None 134 | 135 | def _s_t_functions(self, x, adj): 136 | h = self.rgcn(x, adj) 137 | batch_size = x.shape[0] 138 | if self.apply_batch_norm: 139 | h = self.batch_norm(h) 140 | h = self.lin1(h) 141 | h = self.tanh(h) 142 | h = self.lin2(h) 143 | h = self.rescale(h) 144 | s = h[:, :self.out_size] 145 | t = h[:, self.out_size:] 146 | s = self.sigmoid(s + 2) 147 | 148 | t = t.view(batch_size, 1, self.out_size) 149 | t = t.expand(batch_size, int(self.num_nodes / self.num_masked_cols), self.out_size).to(args.device) 150 | s = s.view(batch_size, 1, self.out_size) 151 | s = s.expand(batch_size, int(self.num_nodes / self.num_masked_cols), self.out_size).to(args.device) 152 | return s, t 153 | 154 | 155 | class AdditiveAdjCoupling(Coupling): 156 | 157 | def __init__(self, num_nodes, num_relations, num_features, mask, 158 | batch_norm=False, 159 | num_masked_cols=1, ch_list=None): 160 | super(AdditiveAdjCoupling, self).__init__(num_nodes, num_relations, 161 | num_features, mask, 162 | batch_norm=batch_norm) 163 | self.num_masked_cols = num_masked_cols 164 | self.adj_size = num_nodes * num_nodes * num_relations 165 | self.out_size = num_nodes * num_relations 166 | self.in_size = self.adj_size - self.out_size 167 | self.mlp = MLP(ch_list, in_size=self.in_size) 168 | self.lin = nn.Linear(ch_list[-1], out_features=self.out_size) 169 | self.batch_norm = nn.BatchNorm1d(self.in_size) 170 | self.scale_factor = torch.zeros(1, device=args.device) 171 | self.tanh = nn.Tanh() 172 | self.rescale = Rescale() 173 | 174 | def forward(self, adj): 175 | masked_adj = adj[:, :, self.mask>0].to(args.device) 176 | t = self._s_t_functions(masked_adj) 177 | t = t.expand(adj.shape) 178 | adj = adj + t * self.inversed_mask 179 | return adj, torch.zeros(1, device=args.device) 180 | 181 | def reverse(self, adj): 182 | masked_adj = adj[:, :, self.mask>0].to(args.device) 183 | t = self._s_t_functions(masked_adj) 184 | t = t.expand(adj.shape) 185 | adj = adj - t * self.inversed_mask 186 | return adj, None 187 | 188 | def _s_t_functions(self, adj): 189 | adj = adj.view(adj.shape[0], -1) 190 | x = adj.clone() 191 | if self.apply_batch_norm: 192 | x = self.batch_norm(x) 193 | y = self.mlp(x) 194 | y = self.tanh(y) 195 | y = self.lin(y) 196 | y = self.rescale(y) 197 | y = y.view(y.shape[0], self.num_relations, self.num_nodes, 1) 198 | return y 199 | 200 | class AdditiveNodeFeatureCoupling(Coupling): 201 | def __init__(self, num_nodes, num_bonds, num_features, 202 | mask, 203 | batch_norm=False, ch_list=None, 204 | input_type='float', num_masked_cols=1): 205 | super(AdditiveNodeFeatureCoupling, self).__init__(num_nodes, num_bonds, 206 | num_features, mask, 207 | batch_norm=batch_norm) 208 | self.num_masked_cols = num_masked_cols 209 | self.out_size = num_features * num_masked_cols 210 | self.rgcn = RGCN(num_features, nhid=128, nout=ch_list['hidden'][0], edge_dim=self.num_bonds, 211 | num_layers=args.num_gcn_layer, dropout=0., normalization=False).to(args.device) 212 | self.lin1 = nn.Linear(ch_list['hidden'][0], out_features=ch_list['hidden'][1]) 213 | self.lin2 = nn.Linear(ch_list['hidden'][1], out_features=self.out_size) 214 | self.scale_factor = torch.zeros(1, device=args.device) 215 | self.batch_norm = nn.BatchNorm1d(ch_list['hidden'][0]) 216 | self.tanh = nn.Tanh() 217 | self.rescale = Rescale() 218 | 219 | def forward(self, x, adj): 220 | masked_x = x * self.mask 221 | batch_size = x.shape[0] 222 | t = self._s_t_functions(masked_x, adj) 223 | t = t.view(batch_size, 1, self.out_size) 224 | t = t.expand(batch_size, int(self.num_nodes/self.num_masked_cols), self.out_size) 225 | if self.num_masked_cols > 1: 226 | t = t.view(batch_size, self.num_nodes, self.num_features) 227 | x = x + t * self.inversed_mask 228 | return x, torch.zeros(1, device=args.device) 229 | 230 | def reverse(self, y, adj): 231 | masked_y = y * self.mask 232 | batch_size = y.shape[0] 233 | t = self._s_t_functions(masked_y, adj) 234 | t = t.view(batch_size, 1, self.out_size) 235 | t = t.expand(batch_size, int(self.num_nodes/self.num_masked_cols), self.out_size) 236 | if self.num_masked_cols > 1: 237 | t = t.view(batch_size, self.num_nodes, self.num_features) 238 | y = y - t * self.inversed_mask 239 | return y, None 240 | 241 | def _s_t_functions(self, x, adj): 242 | h = self.rgcn(x, adj) 243 | if self.apply_batch_norm: 244 | h = self.batch_norm(h) 245 | h = self.lin1(h) 246 | h = self.tanh(h) 247 | h = self.lin2(h) 248 | h = self.rescale(h) 249 | return h 250 | -------------------------------------------------------------------------------- /graph_nvp/hyperparams.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | 4 | import numpy as np 5 | from tabulate import tabulate 6 | 7 | 8 | class Hyperparameters: 9 | def __init__(self, num_nodes=-1, num_relations=-1, num_features=-1, masks=None, path=None, 10 | num_masks=None, mask_size=None, num_coupling=None, batch_norm=False, 11 | additive_transformations=False, learn_dist=True, 12 | squeeze_adj=False, prior_adj_var=1.0, prior_x_var=1.0, 13 | mlp_channels=None, gnn_channels=None, seed=1): 14 | self.gnn_channels = gnn_channels 15 | self.mlp_channels = mlp_channels 16 | self.num_nodes = num_nodes 17 | self.num_relations = num_relations 18 | self.num_features = num_features 19 | self.masks = masks 20 | self.num_masks = num_masks 21 | self.mask_size = mask_size 22 | self.num_coupling = num_coupling 23 | self.path = path 24 | self.apply_batch_norm = batch_norm 25 | self.additive_transformations = additive_transformations 26 | self.learn_dist = learn_dist 27 | self.squeeze_adj = squeeze_adj 28 | self.prior_adj_var = prior_adj_var 29 | self.prior_x_var = prior_x_var 30 | self.seed = seed 31 | 32 | if path is not None: 33 | if os.path.exists(path) and os.path.isfile(path): 34 | with open(path, "r") as f: 35 | obj = json.load(f) 36 | for (key, value) in obj.items(): 37 | if key == 'masks': 38 | masks = dict() 39 | for k in value.keys(): 40 | if value[k]: 41 | dtype = np.bool 42 | masks[k] = [np.array(item, dtype=np.bool) for item in value[k]] 43 | value = masks 44 | setattr(self, key, value) 45 | else: 46 | raise Exception("{} does not exist".format(path)) 47 | 48 | def save(self, path): 49 | self.path = path 50 | with open(path, "w") as f: 51 | json.dump(self.__dict__, f, indent=4, sort_keys=True, cls=NumpyEncoder) 52 | 53 | def print(self): 54 | rows = [] 55 | for key, value in self.__dict__.items(): 56 | rows.append([key, value]) 57 | print(tabulate(rows)) 58 | 59 | 60 | class NumpyEncoder(json.JSONEncoder): 61 | def default(self, obj): 62 | if isinstance(obj, np.ndarray): 63 | return obj.tolist() 64 | return json.JSONEncoder.default(self, obj) 65 | 66 | -------------------------------------------------------------------------------- /graph_nvp/mlp.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | class MLP(nn.Module): 4 | def __init__(self, units, in_size=None): 5 | super(MLP, self).__init__() 6 | assert isinstance(units, (tuple, list)) 7 | assert len(units) >= 1 8 | n_layers = len(units) 9 | 10 | units_list = [in_size] + list(units) 11 | layers = [] 12 | for i in range(n_layers): 13 | layers.append(nn.Linear(units_list[i], units_list[i+1])) 14 | self.layers = nn.Sequential(*layers) 15 | self.n_layers = n_layers 16 | 17 | def forward(self, x): 18 | output = self.layers(x) 19 | return output 20 | -------------------------------------------------------------------------------- /graph_nvp/nvp_model.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | import numpy as np 5 | from utils.argparser import args 6 | from graph_nvp.hyperparams import Hyperparameters 7 | from graph_nvp.coupling import AffineNodeFeatureCoupling, AffineAdjCoupling, \ 8 | AdditiveNodeFeatureCoupling, AdditiveAdjCoupling 9 | 10 | def gaussian_nll(x, mean, ln_var, reduce='sum'): 11 | if reduce not in ('sum', 'mean', 'no'): 12 | raise ValueError( 13 | 'only \'sum\', \'mean\' and \'no\' are valid for \'reduce\', but ' 14 | '\'%s\' is given' % reduce) 15 | 16 | x_prec = torch.exp(-ln_var) 17 | x_diff = x - mean 18 | x_power = (x_diff * x_diff) * x_prec * -0.5 19 | loss = (ln_var + torch.log(torch.tensor([2 * np.pi], device=args.device, dtype=torch.float32))) / 2 - x_power 20 | if reduce == 'sum': 21 | return torch.sum(loss) 22 | elif reduce == 'mean': 23 | return torch.mean(loss) 24 | else: 25 | return loss 26 | 27 | class GraphNvpModel(nn.Module): 28 | def __init__(self, hyperparams: Hyperparameters): 29 | super(GraphNvpModel, self).__init__() 30 | self.hyperparams = hyperparams 31 | self._init_params(hyperparams) 32 | self._need_initialization = False 33 | if self.masks is None: 34 | self._need_initialization = True 35 | self.masks = dict() 36 | self.masks['node'] = self._create_masks('node') 37 | self.masks['channel'] = self._create_masks('channel') 38 | self.num_bonds = self.num_relations 39 | self.num_atoms = self.num_nodes 40 | assert self.num_bonds+1 == self.num_features 41 | self.adj_size = self.num_atoms * self.num_atoms * self.num_relations 42 | self.x_size = self.num_atoms * self.num_features 43 | #self.prior_ln_var = torch.tensor([0.7],device=args.device, dtype=torch.float32) 44 | self.prior_ln_var = nn.Parameter(torch.zeros([1])) 45 | nn.init.constant_(self.prior_ln_var, 1e-5) 46 | self.constant_pi = torch.tensor([3.1415926535], device=args.device, dtype=torch.float32) 47 | # AffineNodeFeatureCoupling found to be unstable. 48 | channel_coupling = AffineNodeFeatureCoupling 49 | node_coupling = AffineAdjCoupling 50 | if self.additive_transformations: 51 | channel_coupling = AdditiveNodeFeatureCoupling 52 | node_coupling = AdditiveAdjCoupling 53 | print("Additive Transformations") 54 | transforms = [] 55 | for i in range(self.num_coupling['channel']): 56 | transforms += [channel_coupling(self.num_nodes, self.num_relations, self.num_features, 57 | self.masks['channel'][i % self.num_masks['channel']], 58 | num_masked_cols=int(self.num_nodes / self.num_masks['channel']), 59 | ch_list=self.gnn_channels, 60 | batch_norm=self.apply_batch_norm)] 61 | for i in range(self.num_coupling['node']): 62 | transforms += [node_coupling(self.num_nodes, self.num_relations, self.num_features, 63 | self.masks['node'][i % self.num_masks['node']], 64 | num_masked_cols=int(self.num_nodes / self.num_masks['channel']), 65 | batch_norm=self.apply_batch_norm, 66 | ch_list=self.mlp_channels)] 67 | self.transforms = nn.ModuleList(transforms) 68 | 69 | def forward(self, adj, x): 70 | h = x.clone() 71 | sum_log_det_jacs_x = torch.zeros(h.shape[0], device=args.device, requires_grad=True) 72 | sum_log_det_jacs_adj = torch.zeros(h.shape[0], device=args.device, requires_grad=True) 73 | # forward step of channel-coupling layers 74 | for i in range(self.num_coupling['channel']): 75 | h, log_det_jacobians = self.transforms[i](h, adj) 76 | sum_log_det_jacs_x = sum_log_det_jacs_x + log_det_jacobians 77 | #adj = adj + 0.9 * torch.rand(adj.shape, device=args.device) 78 | for i in range(self.num_coupling['channel'], len(self.transforms)): 79 | adj, log_det_jacobians = self.transforms[i](adj) 80 | sum_log_det_jacs_adj = sum_log_det_jacs_adj + log_det_jacobians 81 | 82 | adj = adj.view(adj.shape[0], -1) 83 | h = h.view(h.shape[0], -1) 84 | out = [h, adj] 85 | return out, [sum_log_det_jacs_x, sum_log_det_jacs_adj] 86 | 87 | def reverse(self, z, x_size, true_adj=None): 88 | """ 89 | Returns a molecule, given its latent vector. 90 | :param z: latent vector. Shape: [B, N*N*M + N*T] 91 | B = Batch size, N = number of atoms, M = number of bond types, 92 | T = number of atom types (Carbon, Oxygen etc.) 93 | :param true_adj: used for testing. An adjacency matrix of a real molecule 94 | :return: adjacency matrix and feature matrix of a molecule 95 | """ 96 | # NOTE z is a list 97 | batch_size = z.shape[0] 98 | z_x = z[:, :x_size] 99 | z_adj = z[:, x_size:] 100 | temperature = 1.0 101 | if true_adj is None: 102 | h_adj = z_adj.view(batch_size, self.num_relations, self.num_nodes, self.num_nodes) 103 | # First, the adjacency coupling layers are applied in reverse order to get h_adj 104 | for i in reversed(range(self.num_coupling['channel'], len(self.transforms))): 105 | h_adj, log_det_jacobians = self.transforms[i].reverse(h_adj) 106 | adj = h_adj 107 | adj = adj + adj.permute(0, 1, 3, 2) 108 | adj = adj / 2.0 109 | adj = F.softmax(adj, dim=1) 110 | else: 111 | adj = true_adj 112 | 113 | h_x = z_x.view(batch_size, self.num_nodes, self.num_features) 114 | # channel coupling layers 115 | for i in reversed(range(self.num_coupling['channel'])): 116 | h_x, log_det_jacobians = self.transforms[i].reverse(h_x, adj) 117 | return adj, h_x 118 | 119 | def _init_params(self, hyperparams): 120 | self.num_nodes = hyperparams.num_nodes 121 | self.num_relations = hyperparams.num_relations 122 | self.num_features = hyperparams.num_features 123 | self.masks = hyperparams.masks 124 | 125 | self.apply_batch_norm = args.apply_batch_norm 126 | self.additive_transformations = args.additive_transformations 127 | 128 | self.num_masks = hyperparams.num_masks 129 | self.num_coupling = hyperparams.num_coupling 130 | self.mask_size = hyperparams.mask_size 131 | self.mlp_channels = hyperparams.mlp_channels 132 | self.gnn_channels = hyperparams.gnn_channels 133 | 134 | def _create_masks(self, type): 135 | masks = [] 136 | num_cols = int(self.num_nodes / self.hyperparams.num_masks[type]) 137 | if type == 'node': 138 | # Columns of the adjacency matrix is masked 139 | for i in range(self.hyperparams.num_masks[type]): 140 | node_mask = torch.ones([self.num_nodes, self.num_nodes]) 141 | for j in range(num_cols): 142 | node_mask[:, i + j] = 0.0 143 | masks.append(node_mask) 144 | elif type == 'channel': 145 | # One row (one node) of the feature matrix is masked 146 | num_cols = int(self.num_nodes / self.hyperparams.num_masks[type]) 147 | for i in range(self.hyperparams.num_masks[type]): 148 | ch_mask = torch.ones([self.num_nodes, self.num_features]) 149 | for j in range(num_cols): 150 | ch_mask[i * num_cols + j, :] = 0.0 151 | masks.append(ch_mask) 152 | return masks 153 | 154 | def log_prob(self, z, logdet): 155 | # z = [h, adj], logdet: [sum_log_det_jacs_x, sum_log_det_jacs_adj] 156 | logdet[0] = logdet[0] - self.x_size 157 | logdet[1] = logdet[1] - self.adj_size 158 | 159 | ll_node = -1 / 2 * (torch.log(2 * self.constant_pi) + self.prior_ln_var + torch.exp(-self.prior_ln_var) * (z[0] ** 2)) 160 | ll_node = ll_node.sum(-1) # (B) 161 | 162 | ll_edge = -1 / 2 * (torch.log(2 * self.constant_pi) + self.prior_ln_var + torch.exp(-self.prior_ln_var) * (z[1] ** 2)) 163 | ll_edge = ll_edge.sum(-1) # (B) 164 | 165 | ll_node += logdet[0] # ([B]) 166 | ll_edge += logdet[1] # ([B]) 167 | 168 | nll = -(ll_node.mean() / self.x_size + ll_edge.mean() / self.adj_size) / 2.0 169 | return nll 170 | 171 | def save_hyperparams(self, path): 172 | self.hyperparams.save(path) 173 | 174 | def load_hyperparams(self, path): 175 | """ 176 | loads hyper parameters from a json file 177 | :param path: 178 | :return: 179 | """ 180 | hyperparams = Hyperparameters(path=path) 181 | self._init_params(hyperparams) 182 | 183 | -------------------------------------------------------------------------------- /graph_nvp/rgcn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from utils.argparser import args 5 | 6 | def Linear(in_features, out_features, bias=True): 7 | m = nn.Linear(in_features, out_features, bias) 8 | nn.init.xavier_uniform_(m.weight) 9 | if bias: 10 | nn.init.constant_(m.bias, 0.0) 11 | return m 12 | 13 | class Switch(nn.Module): 14 | def __init__(self): 15 | super(Switch, self).__init__() 16 | def forward(self, x): 17 | return x * torch.sigmoid(x) 18 | 19 | class RelationGraphConvolution(nn.Module): 20 | """ 21 | Relation GCN layer. 22 | """ 23 | def __init__(self, in_features, out_features, edge_dim=3, aggregate='sum', dropout=0., use_relu=True, bias=False): 24 | ''' 25 | :param in/out_features: scalar of channels for node embedding 26 | :param edge_dim: dim of edge type, virtual type not included 27 | ''' 28 | super(RelationGraphConvolution, self).__init__() 29 | self.in_features = in_features 30 | self.out_features = out_features 31 | self.edge_dim = edge_dim 32 | self.dropout = dropout 33 | self.aggregate = aggregate 34 | if use_relu: 35 | self.act = nn.ReLU() 36 | elif args.use_switch: 37 | self.act = Switch() 38 | else: 39 | self.act = None 40 | 41 | self.weight = nn.Parameter(torch.FloatTensor( 42 | self.edge_dim, self.in_features, self.out_features)) 43 | if bias: 44 | self.bias = nn.Parameter(torch.FloatTensor( 45 | self.edge_dim, 1, self.out_features)) 46 | else: 47 | self.register_parameter('bias', None) 48 | self.reset_parameters() 49 | self.graph_linear_self = GraphLinear(in_features, out_features) 50 | self.graph_linear_edge = GraphLinear(in_features, out_features * edge_dim) 51 | 52 | def reset_parameters(self): 53 | nn.init.xavier_uniform_(self.weight) 54 | if self.bias is not None: 55 | nn.init.constant_(self.bias, 0.) 56 | 57 | def forward(self, x, adj): 58 | ''' 59 | :param x: (batch, N, d) 60 | :param adj: (batch, E, N, N) 61 | typically d=9 e=3 62 | :return: 63 | updated x with shape (batch, N, d) 64 | ''' 65 | x = F.dropout(x, p=self.dropout, training=self.training) # (b, N, d) 66 | 67 | batch_size = x.size(0) 68 | 69 | mb, node, ch = x.shape 70 | 71 | # --- self connection, apply linear function --- 72 | hs = self.graph_linear_self(x) 73 | # --- relational feature, from neighbor connection --- 74 | # Expected number of neighbors of a vertex 75 | # Since you have to divide by it, if its 0, you need to arbitrarily set it to 1 76 | m = self.graph_linear_edge(x) 77 | m = m.view(mb, node, self.out_features, self.edge_dim) 78 | m = m.permute(0, 3, 1, 2) 79 | # m: (batchsize, edge_type, node, ch) 80 | # hr: (batchsize, edge_type, node, ch) 81 | hr = torch.matmul(adj, m) 82 | # hr: (batchsize, node, ch) 83 | hr = torch.sum(hr, 1)#dim=1) 84 | return hs + hr 85 | 86 | 87 | def __repr__(self): 88 | return self.__class__.__name__ + ' (' + str(self.in_features) + ' -> ' + str(self.out_features) + ')' 89 | 90 | def _get_embs_node(self, x, adj): 91 | """ 92 | Args: 93 | x: current node feature matrix with shape (batch, N, 9) 94 | adj: current adjacency feature matrix with shape (batch, 4, N, N) 95 | Returns: 96 | graph embedding for updating node features with shape (batch, d) 97 | """ 98 | 99 | batch_size = x.size(0) 100 | adj = adj[:, :3] # (batch, 3, N, N) 101 | 102 | node_emb = self.rgcn(x, adj) # (batch, N, d) 103 | if self.is_batchNorm: 104 | node_emb = self.batchNorm(node_emb.transpose(1, 2)).transpose(1, 2) # (batch, N, d) 105 | 106 | graph_emb = torch.sum(node_emb, dim=1, keepdim=False).contiguous() # (batch, d) 107 | return graph_emb 108 | 109 | 110 | class GraphAggregation(nn.Module): 111 | 112 | def __init__(self, in_features=128, out_features=64, b_dim=4, dropout=0.): 113 | super(GraphAggregation, self).__init__() #+b_dim 114 | self.sigmoid_linear = nn.Sequential(nn.Linear(in_features, out_features), 115 | nn.Sigmoid()) #+b_dim 116 | self.tanh_linear = nn.Sequential(nn.Linear(in_features, out_features), 117 | nn.Tanh()) 118 | self.dropout = nn.Dropout(dropout) 119 | self.switch = Switch() 120 | 121 | def forward(self, input, activation): 122 | i = self.sigmoid_linear(input) 123 | j = self.tanh_linear(input) 124 | output = torch.sum(torch.mul(i,j), 1) 125 | if args.use_switch: 126 | output = self.switch(output) 127 | else: 128 | output = activation(output) if activation is not None\ 129 | else output 130 | output = self.dropout(output) 131 | return output 132 | 133 | class GraphLinear(nn.Module): 134 | """Graph Linear layer. 135 | 136 | This function assumes its input is 3-dimensional. 137 | Differently from :class:`chainer.functions.linear`, it applies an affine 138 | transformation to the third axis of input `x`. 139 | 140 | .. seealso:: :class:`torch.nn.Linear` 141 | """ 142 | def __init__(self, *argv, **kwargs): 143 | super(GraphLinear, self).__init__() 144 | #self.linear = spectral_norm(nn.Linear(*argv, **kwargs)) 145 | self.linear = nn.Linear(*argv, **kwargs) 146 | 147 | def __call__(self, x): 148 | """Forward propagation. 149 | Args: 150 | x (:class:`torch.Tensor`) 151 | Input array that should be a float array whose ``dim`` is 3. 152 | Returns: 153 | :class:`torch.Tensor`: 154 | A 3-dimeisional array. 155 | """ 156 | # (minibatch, atom, ch) 157 | s0, s1, s2 = x.size() 158 | x = x.view(s0 * s1, s2) 159 | x = self.linear(x) 160 | x = x.view(s0, s1, -1) 161 | return x 162 | 163 | class RGCN(nn.Module): 164 | def __init__(self, nfeat, nhid=256, nout=128, aggout=64, edge_dim=3, num_layers=3, dropout=0., normalization=False): 165 | ''' 166 | :num_layars: the number of layers in each R-GCN 167 | ''' 168 | super(RGCN, self).__init__() 169 | 170 | self.nfeat = nfeat 171 | self.nhid = nhid 172 | self.nout = nout 173 | self.edge_dim = edge_dim 174 | self.num_layers = num_layers 175 | 176 | self.dropout = dropout 177 | self.emb = Linear(nfeat, nfeat, bias=False) 178 | 179 | self.gc1 = RelationGraphConvolution( 180 | nfeat, nhid, edge_dim=self.edge_dim, aggregate='sum', use_relu=True, dropout=self.dropout, bias=False) 181 | self.gc2 = nn.ModuleList([RelationGraphConvolution(nhid, nhid, edge_dim=self.edge_dim, aggregate='sum', 182 | use_relu=True, dropout=self.dropout, bias=False) 183 | for i in range(self.num_layers-2)]) 184 | self.gc3 = RelationGraphConvolution( 185 | nhid, nout, edge_dim=self.edge_dim, aggregate='sum', use_relu=False, dropout=self.dropout, bias=False) 186 | 187 | self.agg = GraphAggregation(nout, aggout, b_dim=edge_dim, dropout=dropout) 188 | self.output_layer = nn.Linear(aggout, 1) 189 | 190 | def forward(self, x, adj): 191 | ''' 192 | :param x: (batch, N, d) 193 | :param adj: (batch, E, N, N) 194 | :return: 195 | ''' 196 | # TODO: Add normalization for adacency matrix 197 | x = self.emb(x) 198 | x = self.gc1(x, adj) 199 | for i in range(self.num_layers-2): 200 | x = self.gc2[i](x, adj) # (#node, #class) 201 | x = self.gc3(x, adj) # (batch, N, d) 202 | #graph_emb = torch.sum(x, dim=1, keepdim=False).contiguous() 203 | x = self.agg(x, torch.tanh) # [256,64] 204 | return x 205 | 206 | def _get_embs_node(self, x, adj): 207 | """ 208 | Args: 209 | x: current node feature matrix with shape (batch, N, 9) 210 | adj: current adjacency feature matrix with shape (batch, 4, N, N) 211 | Returns: 212 | graph embedding for updating node features with shape (batch, d) 213 | """ 214 | batch_size = x.size(0) 215 | adj = adj[:, :3] # (batch, 3, N, N) 216 | 217 | node_emb = self.rgcn(x, adj) # (batch, N, d) 218 | if self.is_batchNorm: 219 | node_emb = self.batchNorm(node_emb.transpose(1, 2)).transpose(1, 2) # (batch, N, d) 220 | 221 | graph_emb = torch.sum(node_emb, dim=1, keepdim=False).contiguous() # (batch, d) 222 | return graph_emb -------------------------------------------------------------------------------- /train.sh: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env bash 2 | CUDA_VISIBLE_DEVICES=1 python train_mle.py --batch_size 256 \ 3 | --data_file qm9_relgcn_kekulized_ggnp.npz \ 4 | --epochs 10 \ 5 | --device cuda \ 6 | --num_node_masks 9 \ 7 | --num_channel_masks 9 \ 8 | --num_node_coupling 18 \ 9 | --num_channel_coupling 14 \ 10 | --additive_transformations True \ 11 | --apply_batch_norm True \ 12 | --node_mask_size 15 \ 13 | --mode train \ 14 | --num_gen 100 \ 15 | --show_loss_step 5 \ 16 | --gen_path ./results/qm9_mols \ 17 | --model_save_dir ./saved_qm9_models \ 18 | --img_dir=./results/qm9_img 19 | -------------------------------------------------------------------------------- /train_mle.py: -------------------------------------------------------------------------------- 1 | from time import time 2 | from utils.argparser import args 3 | import os, sys, copy 4 | import math 5 | import copy 6 | import random 7 | import numpy as np 8 | import torch 9 | import torch.nn.functional as F 10 | from os.path import join as joinpath 11 | from rdkit import Chem 12 | import chainer 13 | from chainer.datasets import TransformDataset 14 | from chainer.dataset import iterator as iterator_module, convert 15 | from chainer_chemistry.datasets import NumpyTupleDataset 16 | from graph_nvp.hyperparams import Hyperparameters 17 | from graph_nvp.nvp_model import GraphNvpModel 18 | from utils import environment as env 19 | from utils.data_utils import construct_mol, save_mol_png 20 | from tqdm import tqdm 21 | 22 | def read_molecules(path): 23 | f = open(joinpath(path, '{}_config.txt'.format(args.data_name)), 'r') 24 | data_config = eval(f.read()) 25 | f.close() 26 | fp = open(joinpath(args.data_dir, '{}_kekulized_ggnp.txt'.format(args.data_name)), 'r') 27 | all_smiles = [smiles.strip() for smiles in fp] 28 | fp.close() 29 | return data_config, all_smiles 30 | 31 | def sample_z(model, batch_size=args.batch_size, z_mu=None): 32 | z_dim = model.adj_size + model.x_size 33 | mu = np.zeros([z_dim], dtype=np.float32) 34 | sigma_diag = np.ones([z_dim]) 35 | sigma_diag = np.sqrt(np.exp(model.prior_ln_var.item())) * sigma_diag 36 | sigma = args.temp * sigma_diag 37 | 38 | if z_mu is not None: 39 | mu = z_mu 40 | sigma = 0.01 * np.eye(z_dim, dtype=np.float32) 41 | 42 | z = np.random.normal(mu, sigma, (batch_size, z_dim)).astype(np.float32) 43 | z = torch.from_numpy(z).float().to(args.device) 44 | return z.detach() 45 | 46 | def main(): 47 | args.cuda = torch.cuda.is_available() 48 | args.device = 'cuda' if torch.cuda.is_available() else 'cpu' 49 | print(args) 50 | if args.data_name == 'qm9': 51 | from data import transform_qm9 52 | transform_fn = transform_qm9.transform_fn 53 | args.atomic_num_list = [6, 7, 8, 9, 0] 54 | mlp_channels = [256, 256] 55 | gnn_channels = {'gcn': [8, 64], 'hidden': [64, 128]} 56 | valid_idx = transform_qm9.get_val_ids() 57 | elif args.data_name == 'zinc250k': 58 | from data import transform_zinc250k 59 | from data.transform_zinc250k import transform_fn_zinc250k 60 | transform_fn = transform_fn_zinc250k 61 | args.atomic_num_list = [6, 7, 8, 9, 15, 16, 17, 35, 53, 0] 62 | mlp_channels = [1024, 512] 63 | gnn_channels = {'gcn': [16, 128], 'hidden': [64, 256]} 64 | valid_idx = transform_zinc250k.get_val_ids() 65 | 66 | dataset = NumpyTupleDataset.load(joinpath(args.data_dir, args.data_file)) 67 | dataset = TransformDataset(dataset, transform_fn) 68 | 69 | if len(valid_idx) > 0: 70 | train_idx = [t for t in range(len(dataset)) if t not in valid_idx] 71 | n_train = len(train_idx) 72 | train_idx.extend(valid_idx) 73 | train, test = chainer.datasets.split_dataset(dataset, n_train, train_idx) 74 | else: 75 | train, test = chainer.datasets.split_dataset_random(dataset, int(len(dataset) * 0.8), seed=args.seed) 76 | 77 | num_masks = {'node': args.num_node_masks, 'channel': args.num_channel_masks} 78 | mask_size = {'node': args.node_mask_size, 'channel': args.channel_mask_size} 79 | num_coupling = {'node': args.num_node_coupling, 'channel': args.num_channel_coupling} 80 | NVPmodel_params = Hyperparameters(args.num_atoms, args.num_rels, len(args.atomic_num_list), 81 | num_masks=num_masks, mask_size=mask_size, num_coupling=num_coupling, 82 | batch_norm=args.apply_batch_norm, 83 | additive_transformations=args.additive_transformations, 84 | mlp_channels=mlp_channels, 85 | gnn_channels=gnn_channels) 86 | model = GraphNvpModel(NVPmodel_params).to(args.device) 87 | train_iter = chainer.iterators.SerialIterator(train, args.batch_size, repeat=False) 88 | 89 | if isinstance(train_iter, iterator_module.Iterator): 90 | iterator = {'main': train_iter} 91 | # train_dataloader 92 | dataloader = iterator['main'] 93 | data_config, all_train_smiles = read_molecules(args.data_dir) 94 | converter = convert.concat_examples 95 | 96 | # fitting 97 | t_total = time() 98 | total_g_loss, total_d_loss = [], [] 99 | max_size = model.num_atoms # 9 for QM9 100 | num_atom = max_size 101 | node_dim = model.num_features # 5 for QM9 # OR exclude padding dim. 5-1 102 | bond_dim = model.num_bonds # 4 for QM9 103 | best_g_loss, best_d_loss = sys.maxsize, sys.maxsize 104 | start_epoch = args.resume_epoch 105 | if args.resume: 106 | model = GraphNvpModel(hyperparams=NVPmodel_params).to(args.device) 107 | model_path = joinpath(args.model_save_dir, 'epoch{}-mle.ckpt'.format(args.resume_epoch)) 108 | model.load_state_dict(torch.load(model_path, map_location=lambda storage, loc: storage)) 109 | print("Resuming from epoch{}".format(args.resume_epoch)) 110 | 111 | all_unique_rate = [] 112 | all_valid_rate = [] 113 | all_novelty_rate = [] 114 | print('start fitting.') 115 | 116 | optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), args.mle_lr, betas=(args.beta1, args.beta2)) 117 | scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', 118 | factor=args.lr_decay_factor, 119 | patience=args.lr_decay_patience, 120 | min_lr=args.lr_decay_min) 121 | optimizer.step() 122 | 123 | def generate_one(model, mute=False, cnt=None): 124 | """ 125 | inverse flow to generate one molecule 126 | Args: 127 | temp: temperature of normal distributions, we sample from (0, temp^2 * I) 128 | """ 129 | generate_start_t = time() 130 | num2bond = {0: Chem.rdchem.BondType.SINGLE, 1: Chem.rdchem.BondType.DOUBLE, 2: Chem.rdchem.BondType.TRIPLE} 131 | num2bond_symbol = {0: '=', 1: '==', 2: '==='} 132 | num2atom = {0: 6, 1: 7, 2: 8, 3: 9, 4: 15, 5: 16, 6: 17, 7: 35, 8: 53} 133 | num2symbol = {0: 'C', 1: 'N', 2: 'O', 3: 'F', 4: 'P', 5: 'S', 6: 'Cl', 7: 'Br', 8: 'I'} 134 | is_continue = True 135 | mol = None 136 | total_resample = 0 137 | batch_size = 1 138 | # Generating 139 | z = sample_z(model, batch_size=1) 140 | A, X = model.reverse(z, model.x_size) # For QM9: [16,9,9,5], [16,9,5], [16,8]-[B,z_dim] 141 | X = F.softmax(X, dim=2) 142 | mols = [construct_mol(x_elem, adj_elem, args.atomic_num_list) 143 | for x_elem, adj_elem in zip(X, A)] 144 | pure_valid = 0 145 | smiles = '' 146 | num_atoms = -1 147 | for mol in mols: 148 | assert mol is not None, 'mol is None...' 149 | final_valid = env.check_chemical_validity(mol) 150 | valency_valid = env.check_valency(mol) 151 | 152 | if final_valid is False or valency_valid is False: 153 | print('Warning: use valency check during generation but the final molecule is invalid!!!') 154 | continue 155 | num_atoms = mol.GetNumAtoms() 156 | num_bonds = mol.GetNumBonds() 157 | smiles = Chem.MolToSmiles(mol) 158 | 159 | if total_resample == 0: 160 | pure_valid = 1.0 161 | if not mute: 162 | cnt = str(cnt) if cnt is not None else '' 163 | print('smiles%s: %s | #atoms: %d | #bonds: %d | #resample: %.5f | time: %.5f |' % ( 164 | cnt, smiles, num_atoms, num_bonds, total_resample, time() - generate_start_t)) 165 | return smiles, A, X, pure_valid, num_atoms 166 | 167 | def train(model): 168 | for epoch in range(1+start_epoch, args.epochs + 1 - start_epoch): 169 | batch_g_losses = [] 170 | batch_cnt = 0 171 | epoch_example = 0 172 | num_samples = len(dataloader.dataset) 173 | num_batches = math.ceil(num_samples / args.batch_size) 174 | pbar = tqdm(total=num_batches) 175 | 176 | for i_batch, batch_data in enumerate(copy.copy(dataloader)): 177 | batch_time_s = time() 178 | loss = {} 179 | in_arrays = converter(batch_data) 180 | X, A, label = in_arrays[0], in_arrays[1], in_arrays[2] 181 | X, A, label = torch.tensor(X, dtype=torch.float32).to(args.device), \ 182 | torch.tensor(A, dtype=torch.float32).to(args.device), \ 183 | torch.tensor(label, dtype=torch.float32).to(args.device) 184 | # Dequantization 185 | X_prime = X + 0.9 * torch.rand(X.shape, device=args.device) 186 | A_prime = A + 0.9 * torch.rand(A.shape, device=args.device) 187 | z, sum_log_det_jacs = model(A_prime, X_prime) 188 | nll = model.log_prob(z, sum_log_det_jacs) 189 | g_loss = nll 190 | loss['G/loss_g'] = g_loss.item() 191 | batch_g_losses.append(g_loss.item()) 192 | optimizer.zero_grad() 193 | g_loss.backward() 194 | optimizer.step() 195 | scheduler.step(g_loss) 196 | pbar.update() 197 | if i_batch % args.show_loss_step == 0: 198 | tqdm.write("Epoch %d, batch %d, Loss mle: %.5f" % (epoch, i_batch, g_loss.item())) 199 | pbar.close() 200 | 201 | print("Saving GraphNVP model trained with maximum liklihood") 202 | model_path = joinpath(args.model_save_dir, 'epoch{}-mle.ckpt'.format(epoch)) 203 | torch.save(model.state_dict(), model_path) 204 | print('Saved model checkpoints into {}...'.format(args.model_save_dir)) 205 | gen(model, epoch) 206 | 207 | def gen(model, epoch=-1): 208 | model.eval() 209 | all_smiles = [] 210 | pure_valids = [] 211 | appear_in_train = 0. 212 | start_t = time() 213 | cnt_mol = 0 214 | cnt_gen = 0 215 | out_path = joinpath(args.gen_path, 'mle_mols{}.txt'.format(epoch)) 216 | print("Generating %d mols for evaluation" % (args.num_gen)) 217 | while cnt_mol < args.num_gen: 218 | smiles, A, X, no_resample, num_atoms = generate_one(model, mute=False, cnt=cnt_gen) 219 | cnt_gen += 1 220 | if cnt_gen > args.max_resample: 221 | break 222 | if num_atoms < 0 or num_atoms < args.min_atoms: 223 | print('#atoms of generated molecule less than %d, discarded!' % args.min_atoms) 224 | continue 225 | else: 226 | cnt_mol += 1 227 | if cnt_mol % 100 == 0: 228 | print('cur cnt mol: %d' % cnt_mol) 229 | all_smiles.append(smiles) 230 | pure_valids.append(no_resample) 231 | print('Accepting: {}'.format(smiles)) 232 | if all_train_smiles is not None and smiles in all_train_smiles: 233 | appear_in_train += 1.0 234 | mol = Chem.MolFromSmiles(smiles) 235 | qed_score = env.qed(mol) 236 | plogp_score = env.penalized_logp(mol) 237 | if cnt_mol > args.num_gen: 238 | print("Generating {} times rather than 100 times!".format(cnt_mol)) 239 | args.num_gen = cnt_mol 240 | 241 | unique_smiles = list(set(all_smiles)) 242 | unique_rate = len(unique_smiles) / args.num_gen 243 | pure_valid_rate = sum(pure_valids) / args.num_gen 244 | novelty = 1. - (appear_in_train / args.num_gen) 245 | 246 | print('Time for generating (%d/%d) molecules(#atoms>=%d) with %d resamplings: %.5f' % ( 247 | cnt_gen-args.max_resample, args.num_gen, args.min_atoms, args.max_resample, time() - start_t)) 248 | print('| unique rate: %.5f | valid rate: %.5f | novelty: %.5f |' % (unique_rate, pure_valid_rate, novelty)) 249 | mol_img_dir = joinpath(args.img_dir, 'mol_img{}'.format(epoch)) 250 | os.makedirs(mol_img_dir, exist_ok=True) 251 | if not os.path.exists(args.gen_path): 252 | os.makedirs(args.gen_path) 253 | if out_path is not None and args.save: 254 | with open(out_path, 'w+') as out_file: 255 | cnt = 0 256 | for i, mol in enumerate(all_smiles): 257 | # Invalid disconnection 258 | if '.' in all_smiles[i]: 259 | continue 260 | out_file.write(all_smiles[i] + '\n') 261 | save_mol_png(Chem.MolFromSmiles(mol), joinpath(mol_img_dir, '{}.png'.format(i))) 262 | cnt += 1 263 | print('writing %d smiles into %s done!' % (cnt, out_path)) 264 | all_unique_rate.append(unique_rate) 265 | all_valid_rate.append(pure_valid_rate) 266 | all_novelty_rate.append(novelty) 267 | if args.save: 268 | print('saving metric of validity, novelty and uniqueness into %s' %(args.gen_path)) 269 | np.save(joinpath(args.gen_path, 'valid{}'.format(epoch)), np.array(all_valid_rate)) 270 | np.save(joinpath(args.gen_path, 'novelty{}'.format(epoch)), np.array(all_novelty_rate)) 271 | np.save(joinpath(args.gen_path, 'unique{}'.format(epoch)), np.array(all_unique_rate)) 272 | 273 | if args.mode == 'train': 274 | train(model) 275 | elif args.mode == 'gen': 276 | gen(model) 277 | else: 278 | print("Specify mode as 'train' or 'gen'") 279 | 280 | if __name__ == '__main__': 281 | random.seed(args.seed) 282 | np.random.seed(args.seed) 283 | torch.manual_seed(args.seed) 284 | main() -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hlzhang109/PyTorch-GraphNVP/eb6cabd1e5e39f84ffe92662ef5d667aa4e30db3/utils/__init__.py -------------------------------------------------------------------------------- /utils/argparser.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | from distutils.util import strtobool 3 | 4 | def get_parser(): 5 | parser = argparse.ArgumentParser(description='argparser') 6 | # data I/O 7 | parser.add_argument('--data_dir', type=str, default='./data', help='Location for the dataset') 8 | parser.add_argument('--data_path', type=str, default='./data/qm9', help='path for loading data and dataconfig') 9 | parser.add_argument('--data_name', type=str, default='qm9', choices=['qm9', 'zinc250k'], help='dataset name') 10 | parser.add_argument('--data_file', type=str, default='qm9_relgcn_kekulized_ggnp.npz', help='Name of the dataset') 11 | parser.add_argument('--save_dir', type=str, default='./results', 12 | help='Location for parameter checkpoints and samples') 13 | parser.add_argument('--epochs', type=int, default=20, help='Num of epochs to run in total') 14 | parser.add_argument('--device', type=str, default='cuda', help='cpu or cuda') 15 | # reproducibility 16 | parser.add_argument('--seed', type=int, default=1, help='Random seed to use') 17 | parser.add_argument('--num_atoms', type=int, default=9, help='Maximum number of atoms in a molecule') 18 | parser.add_argument('--num_rels', type=int, default=4, help='Number of bond types') 19 | parser.add_argument('--num_atom_types', type=int, default=4, help='Types of atoms that can be used in a molecule') 20 | parser.add_argument('--num_node_masks', type=int, default=9, 21 | help='Number of node masks to be used in coupling layers') 22 | parser.add_argument('--num_channel_masks', type=int, default=4, 23 | help='Number of channel masks to be used in coupling layers') 24 | parser.add_argument('--num_node_coupling', type=int, default=12, help='Number of coupling layers with node masking') 25 | parser.add_argument('--num_channel_coupling', type=int, default=6, 26 | help='Number of coupling layers with channel masking') 27 | parser.add_argument('--node_mask_size', type=int, default=5, help='Number of cells to be masked in the Node ' 28 | 'coupling layer') 29 | parser.add_argument('--channel_mask_size', type=int, default=-1, help='Number of cells to be masked in the Channel ' 30 | 'coupling layer') 31 | parser.add_argument('--apply_batch_norm', type=bool, default=False, help='Whether batch ' 32 | 'normalization should be performed') 33 | parser.add_argument('--additive_transformations', type=bool, default=True, 34 | help='if True, apply only addictive coupling layers; else, apply affine coupling layers') 35 | # Model configuration. 36 | parser.add_argument('--temp', type=float, default=0.7, help='the tempearture of mcmc steps') 37 | parser.add_argument('--use_switch', type=bool, default=False, help='use switch activation for R-GCN') 38 | parser.add_argument('--num_gcn_layer', type=int, default=3) 39 | parser.add_argument('--show_loss_step', type=int, default=10, help='show loss every n step/epoch') 40 | 41 | # Training configuration 42 | parser.add_argument('--batch_size', type=int, default=16, help='mini-batch size') 43 | parser.add_argument('--mle_lr', type=float, default=0.001, help='learning rate for MLE') 44 | parser.add_argument('-lr_decay_factor', default=0.5, type=float, help='learning rate decay factor') 45 | parser.add_argument('-lr_decay_patience', default=200, type=float, help='learning rate decay patience') 46 | parser.add_argument('-lr_decay_min', default=0.00001, type=float, help='learning rate decay min') 47 | parser.add_argument('--dropout', type=float, default=0., help='dropout rate') 48 | parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for Adam optimizer') 49 | parser.add_argument('--beta2', type=float, default=0.9, help='beta2 for Adam optimizer') 50 | parser.add_argument('--resume_epoch', type=int, default=0, help='resume training after this epoch') 51 | parser.add_argument('--resume', type=bool, default=False, help='resume training') 52 | 53 | # Generation args 54 | parser.add_argument('--min_atoms', type=int, default=2, help='Minimum number of atoms in a generated molecule') 55 | parser.add_argument('--num_gen', type=int, default=100, help='num of molecules to generate on each call to train.generate') 56 | parser.add_argument('--min_gen_epoch', type=int, default=5, help='num of molecules to generate on each call to train.generate') 57 | parser.add_argument('--max_resample', type=int, default=200, help='the times of resampling each epoch') 58 | parser.add_argument('--atomic_num_list', type=int, default=[6, 7, 8, 9, 0], 59 | help='atomic number list for datasets') 60 | #parser.add_argument('--post_method', type=str, default='softmax', choices=['softmax', 'soft_gumbel', 'hard_gumbel'], 61 | # help='TODO: postprocessing to convert continuous A and X to discrete ones') 62 | # Miscellaneous. 63 | parser.add_argument('--num_workers', type=int, default=1) 64 | parser.add_argument('--mode', type=str, default='train', choices=['train', 'gen']) 65 | parser.add_argument('--save', action='store_true', default=True, help='Save model.') 66 | # Directories. 67 | parser.add_argument('--log_dir', type=str, default='./logs') 68 | parser.add_argument('--model_save_dir', type=str, default='./saved_models') 69 | parser.add_argument('--sample_dir', type=str, default='./samples') 70 | parser.add_argument('--result_dir', type=str, default='./results') 71 | parser.add_argument('--img_dir', type=str, default='./results/qm9_img') 72 | parser.add_argument('--gen_path', type=str, default='./results/mols', help='output path for generated mol') 73 | parser.add_argument('--model_name', type=str, default='mle', help='model name, crucial for test and checkpoint initialization [epoch3_1gpu]') 74 | return parser 75 | 76 | parser = get_parser() 77 | args = parser.parse_args() 78 | 79 | #Step size. 80 | #parser.add_argument('--log_step', type=int, default=10) 81 | #parser.add_argument('--sample_step', type=int, default=1000) 82 | #parser.add_argument('--model_save_step', type=int, default=1000) 83 | #parser.add_argument('--lr_update_step', type=int, default=1000) -------------------------------------------------------------------------------- /utils/data_utils.py: -------------------------------------------------------------------------------- 1 | import chainer 2 | import chainer.functions as chainerF 3 | import numpy as np 4 | from chainer.backends import cuda 5 | from rdkit import Chem 6 | from rdkit.Chem import Draw 7 | import torch 8 | 9 | atom_decoder_m = {0: 6, 1: 7, 2: 8, 3: 9, 4: 15, 5: 16, 6: 17, 7: 35, 8: 53} 10 | bond_decoder_m = {1: Chem.rdchem.BondType.SINGLE, 2: Chem.rdchem.BondType.DOUBLE, 3: Chem.rdchem.BondType.TRIPLE} 11 | 12 | 13 | def flatten_graph_data(adj, x): 14 | return torch.cat((adj.view(adj.shape[0], -1), 15 | x.view(x.shape[0], -1)), -1) 16 | 17 | def split_channel(x): 18 | n = x.shape[1] // 2 19 | return x[:, :n], x[:, n:] 20 | 21 | def get_graph_data(x, num_nodes, num_relations, num_features): 22 | """ 23 | Converts a vector of shape [b, num_nodes, m] to Adjacency matrix 24 | of shape [b, num_relations, num_nodes, num_nodes] 25 | and a feature matrix of shape [b, num_nodes, num_features]. 26 | :param x: 27 | :param num_nodes: 28 | :param num_relations: 29 | :param num_features: 30 | :return: 31 | """ 32 | adj = x[:, :num_nodes*num_nodes*num_relations].view( 33 | -1, num_relations, num_nodes, num_nodes) 34 | feat_mat = x[:, num_nodes*num_nodes*num_relations:].view( 35 | -1, num_nodes, num_features) 36 | return adj, feat_mat 37 | 38 | 39 | def Tensor2Mol(A, x): 40 | mol = Chem.RWMol() 41 | # x[x < 0] = 0. 42 | # A[A < 0] = -1 43 | # atoms_exist = np.sum(x, 1) != 0 44 | atoms = torch.argmax(x, 1) 45 | atoms_exist = atoms != 4 46 | atoms = atoms[atoms_exist] 47 | atoms += 6 48 | adj = torch.argmax(A, 0) 49 | #adj = np.array(adj) 50 | adj = adj[atoms_exist, :][:, atoms_exist] 51 | adj[adj == 3] = -1 52 | adj += 1 53 | # print('num atoms: {}'.format(sum(atoms>0))) 54 | 55 | for atom in atoms: 56 | mol.AddAtom(Chem.Atom(int(atom))) 57 | 58 | for start, end in torch.nonzero(adj): 59 | if start > end: 60 | mol.AddBond(int(start), int(end), bond_decoder_m[adj[int(start), int(end)]]) 61 | 62 | return mol 63 | 64 | 65 | def construct_mol(X, A, atomic_num_list): 66 | mol = Chem.RWMol() 67 | # x (ch, num_node) 68 | atoms = torch.argmax(X, axis=1) 69 | # last a 70 | atoms_exist = atoms != len(atomic_num_list) - 1 71 | atoms = atoms[atoms_exist] 72 | # print('num atoms: {}'.format(sum(atoms>0))) 73 | 74 | for atom in atoms: 75 | mol.AddAtom(Chem.Atom(int(atomic_num_list[atom]))) 76 | 77 | # A (edge_type, num_node, num_node) 78 | adj = torch.argmax(A, axis=0) 79 | adj = adj[atoms_exist, :][:, atoms_exist] 80 | adj[adj == 3] = -1 81 | adj += 1 82 | for start, end in torch.nonzero(adj): 83 | if start > end: 84 | mol.AddBond(start.int().item(), end.int().item(), \ 85 | bond_decoder_m[int(adj[start.int().item(), end.int().item()])]) 86 | return mol 87 | 88 | 89 | def valid_mol(x): 90 | s = Chem.MolFromSmiles(Chem.MolToSmiles(x)) if x is not None else None 91 | if s is not None and '.' not in Chem.MolToSmiles(s): 92 | return s 93 | return None 94 | 95 | 96 | def check_tensor(x): 97 | return valid_mol(Tensor2Mol(*x)) 98 | 99 | 100 | def adj_to_smiles(adj, x, atomic_num_list, gpu=-1): 101 | valid = [Chem.MolToSmiles(construct_mol(x_elem, adj_elem, atomic_num_list)) 102 | for x_elem, adj_elem in zip(x, adj)] 103 | return valid 104 | 105 | 106 | def check_validity(adj, x, atomic_num_list, gpu=-1, return_unique=True): 107 | valid = [valid_mol(construct_mol(x_elem, adj_elem, atomic_num_list)) 108 | for x_elem, adj_elem in zip(x, adj)] 109 | valid = [mol for mol in valid if mol is not None] 110 | print("valid molecules: {}/{}".format(len(valid), adj.shape[0])) 111 | for i, mol in enumerate(valid): 112 | print("[{}] {}".format(i, Chem.MolToSmiles(mol))) 113 | 114 | n_mols = x.shape[0] 115 | valid_ratio = len(valid)/n_mols 116 | valid_smiles = [Chem.MolToSmiles(mol) for mol in valid] 117 | unique_smiles = list(set(valid_smiles)) 118 | unique_ratio = 0. 119 | if len(valid) > 0: 120 | unique_ratio = len(unique_smiles)/len(valid) 121 | if return_unique: 122 | valid_smiles = unique_smiles 123 | valid_mols = [Chem.MolFromSmiles(s) for s in valid_smiles] 124 | print("valid: {:.3f}%, unique: {:.3f}%".format(valid_ratio * 100, unique_ratio * 100)) 125 | 126 | results = dict() 127 | results['valid_mols'] = valid_mols 128 | results['valid_smiles'] = valid_smiles 129 | results['valid_ratio'] = valid_ratio*100 130 | results['unique_ratio'] = unique_ratio*100 131 | 132 | return results 133 | 134 | 135 | def check_novelty(gen_smiles, train_smiles): 136 | if len(gen_smiles) == 0: 137 | novel_ratio = 0. 138 | else: 139 | duplicates = [1 for mol in gen_smiles if mol in train_smiles] 140 | novel = len(gen_smiles) - sum(duplicates) 141 | novel_ratio = novel*100./len(gen_smiles) 142 | print("novelty: {}%".format(novel_ratio)) 143 | return novel_ratio 144 | 145 | def save_mol_png(mol, filepath, size=(600, 600)): 146 | Draw.MolToFile(mol, filepath, size=size) -------------------------------------------------------------------------------- /utils/environment.py: -------------------------------------------------------------------------------- 1 | import itertools 2 | import numpy as np 3 | from rdkit import Chem 4 | from rdkit.Chem import AllChem 5 | from rdkit.Chem.Descriptors import qed, MolLogP 6 | from rdkit.Chem import rdMolDescriptors 7 | from rdkit.Chem.FilterCatalog import FilterCatalogParams, FilterCatalog 8 | import copy 9 | import networkx as nx 10 | import math 11 | import random 12 | import time 13 | # import matplotlib.pyplot as plt 14 | import csv 15 | 16 | from contextlib import contextmanager 17 | import sys, os 18 | 19 | from utils.sascorer import calculateScore 20 | 21 | # most of the codes here are taken from GCPN's open-source implementation 22 | ''' 23 | Environment Usage 24 | plogp: penalized_logp(mol) 25 | logp: MolLogP(mol) 26 | qed: qed(mol) 27 | sa: calculateScore(mol) 28 | mw: rdMolDescriptors.CalcExactMolWt(mol) 29 | ''' 30 | 31 | 32 | def convert_radical_electrons_to_hydrogens(mol): 33 | """ 34 | Converts radical electrons in a molecule into bonds to hydrogens. Only 35 | use this if molecule is valid. Results a new mol object 36 | :param mol: rdkit mol object 37 | :return: rdkit mol object 38 | """ 39 | m = copy.deepcopy(mol) 40 | if Chem.Descriptors.NumRadicalElectrons(m) == 0: # not a radical 41 | return m 42 | else: # a radical 43 | print('converting radical electrons to H') 44 | for a in m.GetAtoms(): 45 | num_radical_e = a.GetNumRadicalElectrons() 46 | if num_radical_e > 0: 47 | a.SetNumRadicalElectrons(0) 48 | a.SetNumExplicitHs(num_radical_e) 49 | return m 50 | 51 | 52 | def check_chemical_validity(mol): 53 | """ 54 | Checks the chemical validity of the mol object. Existing mol object is 55 | not modified. Radicals pass this test. 56 | :return: True if chemically valid, False otherwise 57 | """ 58 | s = Chem.MolToSmiles(mol, isomericSmiles=True) 59 | m = Chem.MolFromSmiles(s) # implicitly performs sanitization 60 | if m: 61 | return True 62 | else: 63 | return False 64 | 65 | 66 | def check_valency(mol): 67 | """ 68 | Checks that no atoms in the mol have exceeded their possible 69 | valency 70 | :return: True if no valency issues, False otherwise 71 | """ 72 | try: 73 | Chem.SanitizeMol(mol, 74 | sanitizeOps=Chem.SanitizeFlags.SANITIZE_PROPERTIES) 75 | return True 76 | except ValueError: 77 | return False 78 | 79 | 80 | def get_final_smiles(mol): 81 | """ 82 | Returns a SMILES of the final molecule. Converts any radical 83 | electrons into hydrogens. Works only if molecule is valid 84 | :return: SMILES 85 | """ 86 | m = convert_radical_electrons_to_hydrogens(mol) 87 | return Chem.MolToSmiles(m, isomericSmiles=True) 88 | 89 | 90 | def get_final_mol(mol): 91 | """ 92 | Returns a rdkit mol object of the final molecule. Converts any radical 93 | electrons into hydrogens. Works only if molecule is valid 94 | :return: SMILES 95 | """ 96 | m = convert_radical_electrons_to_hydrogens(mol) 97 | return m 98 | 99 | 100 | def add_hydrogen(mol): 101 | s = Chem.MolToSmiles(mol, isomericSmiles=True) 102 | return Chem.MolFromSmiles(s) 103 | 104 | 105 | def calculate_min_plogp(mol): 106 | p1 = penalized_logp(mol) 107 | s1 = Chem.MolToSmiles(mol, isomericSmiles=True) 108 | s2 = Chem.MolToSmiles(mol, isomericSmiles=False) 109 | mol1 = Chem.MolFromSmiles(s1) 110 | mol2 = Chem.MolFromSmiles(s2) 111 | p2 = penalized_logp(mol1) 112 | p3 = penalized_logp(mol2) 113 | final_p = min(p1, p2) 114 | final_p = min(final_p, p3) 115 | return final_p 116 | 117 | 118 | def penalized_logp(mol): 119 | """ 120 | Reward that consists of log p penalized by SA and # long cycles, 121 | as described in (Kusner et al. 2017). Scores are normalized based on the 122 | statistics of 250k_rndm_zinc_drugs_clean.smi dataset 123 | :param mol: rdkit mol object 124 | :return: float 125 | """ 126 | # normalization constants, statistics from 250k_rndm_zinc_drugs_clean.smi 127 | logP_mean = 2.4570953396190123 128 | logP_std = 1.434324401111988 129 | SA_mean = -3.0525811293166134 130 | SA_std = 0.8335207024513095 131 | cycle_mean = -0.0485696876403053 132 | cycle_std = 0.2860212110245455 133 | 134 | log_p = MolLogP(mol) 135 | SA = -calculateScore(mol) 136 | 137 | # cycle score 138 | cycle_list = nx.cycle_basis(nx.Graph( 139 | Chem.rdmolops.GetAdjacencyMatrix(mol))) 140 | if len(cycle_list) == 0: 141 | cycle_length = 0 142 | else: 143 | cycle_length = max([len(j) for j in cycle_list]) 144 | if cycle_length <= 6: 145 | cycle_length = 0 146 | else: 147 | cycle_length = cycle_length - 6 148 | cycle_score = -cycle_length 149 | 150 | normalized_log_p = (log_p - logP_mean) / logP_std 151 | normalized_SA = (SA - SA_mean) / SA_std 152 | normalized_cycle = (cycle_score - cycle_mean) / cycle_std 153 | 154 | return normalized_log_p + normalized_SA + normalized_cycle 155 | 156 | 157 | def steric_strain_filter(mol, cutoff=0.82, max_attempts_embed=20, max_num_iters=200): 158 | """ 159 | Flags molecules based on a steric energy cutoff after max_num_iters 160 | iterations of MMFF94 forcefield minimization. Cutoff is based on average 161 | angle bend strain energy of molecule 162 | :param mol: rdkit mol object 163 | :param cutoff: kcal/mol per angle . If minimized energy is above this 164 | threshold, then molecule fails the steric strain filter 165 | :param max_attempts_embed: number of attempts to generate initial 3d 166 | coordinates 167 | :param max_num_iters: number of iterations of forcefield minimization 168 | :return: True if molecule could be successfully minimized, and resulting 169 | energy is below cutoff, otherwise False 170 | """ 171 | # check for the trivial cases of a single atom or only 2 atoms, in which 172 | # case there is no angle bend strain energy (as there are no angles!) 173 | if mol.GetNumAtoms() <= 2: 174 | return True 175 | 176 | # make copy of input mol and add hydrogens 177 | m = copy.deepcopy(mol) 178 | m_h = Chem.AddHs(m) 179 | 180 | # generate an initial 3d conformer 181 | try: 182 | flag = AllChem.EmbedMolecule(m_h, maxAttempts=max_attempts_embed) 183 | if flag == -1: 184 | # print("Unable to generate 3d conformer") 185 | return False 186 | except: # to catch error caused by molecules such as C=[SH]1=C2OC21ON(N)OC(=O)NO 187 | # print("Unable to generate 3d conformer") 188 | return False 189 | 190 | # set up the forcefield 191 | AllChem.MMFFSanitizeMolecule(m_h) 192 | if AllChem.MMFFHasAllMoleculeParams(m_h): 193 | mmff_props = AllChem.MMFFGetMoleculeProperties(m_h) 194 | try: # to deal with molecules such as CNN1NS23(=C4C5=C2C(=C53)N4Cl)S1 195 | ff = AllChem.MMFFGetMoleculeForceField(m_h, mmff_props) 196 | except: 197 | # print("Unable to get forcefield or sanitization error") 198 | return False 199 | else: 200 | # print("Unrecognized atom type") 201 | return False 202 | 203 | # minimize steric energy 204 | try: 205 | ff.Minimize(maxIts=max_num_iters) 206 | except: 207 | # print("Minimization error") 208 | return False 209 | 210 | # ### debug ### 211 | # min_e = ff.CalcEnergy() 212 | # print("Minimized energy: {}".format(min_e)) 213 | # ### debug ### 214 | 215 | # get the angle bend term contribution to the total molecule strain energy 216 | mmff_props.SetMMFFBondTerm(False) 217 | mmff_props.SetMMFFAngleTerm(True) 218 | mmff_props.SetMMFFStretchBendTerm(False) 219 | mmff_props.SetMMFFOopTerm(False) 220 | mmff_props.SetMMFFTorsionTerm(False) 221 | mmff_props.SetMMFFVdWTerm(False) 222 | mmff_props.SetMMFFEleTerm(False) 223 | 224 | ff = AllChem.MMFFGetMoleculeForceField(m_h, mmff_props) 225 | 226 | min_angle_e = ff.CalcEnergy() 227 | # print("Minimized angle bend energy: {}".format(min_angle_e)) 228 | 229 | # find number of angles in molecule 230 | # from molecule... This is too hacky 231 | num_atoms = m_h.GetNumAtoms() 232 | atom_indices = range(num_atoms) 233 | angle_atom_triplets = itertools.permutations(atom_indices, 3) # get all 234 | # possible 3 atom indices groups. Currently, each angle is represented by 235 | # 2 duplicate groups. Should remove duplicates here to be more efficient 236 | double_num_angles = 0 237 | for triplet in list(angle_atom_triplets): 238 | if mmff_props.GetMMFFAngleBendParams(m_h, *triplet): 239 | double_num_angles += 1 240 | num_angles = double_num_angles / 2 # account for duplicate angles 241 | 242 | # print("Number of angles: {}".format(num_angles)) 243 | 244 | avr_angle_e = min_angle_e / num_angles 245 | 246 | # print("Average minimized angle bend energy: {}".format(avr_angle_e)) 247 | if avr_angle_e < cutoff: 248 | return True 249 | else: 250 | return False 251 | 252 | ### YES/NO filters ### 253 | def zinc_molecule_filter(mol): 254 | """ 255 | Flags molecules based on problematic functional groups as 256 | provided set of ZINC rules from 257 | http://blaster.docking.org/filtering/rules_default.txt. 258 | :param mol: rdkit mol object 259 | :return: Returns True if molecule is okay (ie does not match any of 260 | therules), False if otherwise 261 | """ 262 | params = FilterCatalogParams() 263 | params.AddCatalog(FilterCatalogParams.FilterCatalogs.ZINC) 264 | catalog = FilterCatalog(params) 265 | return not catalog.HasMatch(mol) -------------------------------------------------------------------------------- /utils/fpscores.pkl.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hlzhang109/PyTorch-GraphNVP/eb6cabd1e5e39f84ffe92662ef5d667aa4e30db3/utils/fpscores.pkl.gz -------------------------------------------------------------------------------- /utils/molecular_metrics.py: -------------------------------------------------------------------------------- 1 | """ 2 | Original code from MolGAN by @nicola-decao under MIT license 3 | https://github.com/nicola-decao/MolGAN/blob/master/utils/molecular_metrics.py 4 | """ 5 | import pickle 6 | import gzip 7 | from rdkit import DataStructs 8 | from rdkit import Chem 9 | from rdkit.Chem import QED 10 | from rdkit.Chem import Crippen 11 | 12 | import math 13 | import numpy as np 14 | 15 | # NP_model = pickle.load(gzip.open('data/NP_score.pkl.gz')) 16 | # SA_model = {i[j]: float(i[0]) for i in pickle.load(gzip.open('data/SA_score.pkl.gz')) for j in range(1, len(i))} 17 | NP_model = None 18 | SA_model = None 19 | 20 | 21 | class MolecularMetrics(object): 22 | 23 | @staticmethod 24 | def _avoid_sanitization_error(op): 25 | try: 26 | return op() 27 | except ValueError: 28 | return None 29 | 30 | @staticmethod 31 | def remap(x, x_min, x_max): 32 | return (x - x_min) / (x_max - x_min) 33 | 34 | @staticmethod 35 | def valid_lambda(x): 36 | return x is not None and Chem.MolToSmiles(x) != '' 37 | 38 | @staticmethod 39 | def valid_lambda_special(x): 40 | s = Chem.MolToSmiles(x) if x is not None else '' 41 | return x is not None and '*' not in s and '.' not in s and s != '' 42 | 43 | @staticmethod 44 | def valid_scores(mols): 45 | return np.array(list(map(MolecularMetrics.valid_lambda_special, mols)), dtype=np.float32) 46 | 47 | @staticmethod 48 | def valid_filter(mols): 49 | return list(filter(MolecularMetrics.valid_lambda, mols)) 50 | 51 | @staticmethod 52 | def valid_total_score(mols): 53 | return np.array(list(map(MolecularMetrics.valid_lambda, mols)), dtype=np.float32).mean() 54 | 55 | @staticmethod 56 | def novel_scores(mols, data): 57 | return np.array( 58 | list(map(lambda x: MolecularMetrics.valid_lambda(x) and Chem.MolToSmiles(x) not in data.smiles, mols))) 59 | 60 | @staticmethod 61 | def novel_filter(mols, data): 62 | return list(filter(lambda x: MolecularMetrics.valid_lambda(x) and Chem.MolToSmiles(x) not in data.smiles, mols)) 63 | 64 | @staticmethod 65 | def novel_total_score(mols, data): 66 | return MolecularMetrics.novel_scores(MolecularMetrics.valid_filter(mols), data).mean() 67 | 68 | @staticmethod 69 | def unique_scores(mols): 70 | smiles = list(map(lambda x: Chem.MolToSmiles(x) if MolecularMetrics.valid_lambda(x) else '', mols)) 71 | return np.clip( 72 | 0.75 + np.array(list(map(lambda x: 1 / smiles.count(x) if x != '' else 0, smiles)), dtype=np.float32), 0, 1) 73 | 74 | @staticmethod 75 | def unique_total_score(mols): 76 | v = MolecularMetrics.valid_filter(mols) 77 | s = set(map(lambda x: Chem.MolToSmiles(x), v)) 78 | return 0 if len(v) == 0 else len(s) / len(v) 79 | 80 | # @staticmethod 81 | # def novel_and_unique_total_score(mols, data): 82 | # return ((MolecularMetrics.unique_scores(mols) == 1).astype(float) * MolecularMetrics.novel_scores(mols, 83 | # data)).sum() 84 | # 85 | # @staticmethod 86 | # def reconstruction_scores(data, model, session, sample=False): 87 | # 88 | # m0, _, _, a, x, _, f, _, _ = data.next_validation_batch() 89 | # feed_dict = {model.edges_labels: a, model.nodes_labels: x, model.node_features: f, model.training: False} 90 | # 91 | # try: 92 | # feed_dict.update({model.variational: False}) 93 | # except AttributeError: 94 | # pass 95 | # 96 | # n, e = session.run([model.nodes_gumbel_argmax, model.edges_gumbel_argmax] if sample else [ 97 | # model.nodes_argmax, model.edges_argmax], feed_dict=feed_dict) 98 | # 99 | # n, e = np.argmax(n, axis=-1), np.argmax(e, axis=-1) 100 | # 101 | # m1 = [data.matrices2mol(n_, e_, strict=True) for n_, e_ in zip(n, e)] 102 | # 103 | # return np.mean([float(Chem.MolToSmiles(m0_) == Chem.MolToSmiles(m1_)) if m1_ is not None else 0 104 | # for m0_, m1_ in zip(m0, m1)]) 105 | 106 | @staticmethod 107 | def natural_product_scores(mols, norm=False): 108 | 109 | # calculating the score 110 | scores = [sum(NP_model.get(bit, 0) 111 | for bit in Chem.rdMolDescriptors.GetMorganFingerprint(mol, 112 | 2).GetNonzeroElements()) / float( 113 | mol.GetNumAtoms()) if mol is not None else None 114 | for mol in mols] 115 | 116 | # preventing score explosion for exotic molecules 117 | scores = list(map(lambda score: score if score is None else ( 118 | 4 + math.log10(score - 4 + 1) if score > 4 else ( 119 | -4 - math.log10(-4 - score + 1) if score < -4 else score)), scores)) 120 | 121 | scores = np.array(list(map(lambda x: -4 if x is None else x, scores))) 122 | scores = np.clip(MolecularMetrics.remap(scores, -3, 1), 0.0, 1.0) if norm else scores 123 | 124 | return scores 125 | 126 | @staticmethod 127 | def quantitative_estimation_druglikeness_scores(mols, norm=False): 128 | return np.array(list(map(lambda x: 0 if x is None else x, [ 129 | MolecularMetrics._avoid_sanitization_error(lambda: QED.qed(mol)) if mol is not None else None for mol in 130 | mols]))) 131 | 132 | @staticmethod 133 | def water_octanol_partition_coefficient_scores(mols, norm=False): 134 | scores = [MolecularMetrics._avoid_sanitization_error(lambda: Crippen.MolLogP(Chem.AddHs(mol, 1))) if mol is not None else None 135 | for mol in mols] 136 | scores = np.array(list(map(lambda x: -3 if x is None else x, scores))) 137 | scores = np.clip(MolecularMetrics.remap(scores, -2.12178879609, 6.0429063424), 0.0, 1.0) if norm else scores 138 | 139 | return scores 140 | 141 | @staticmethod 142 | def _compute_SAS(mol): 143 | fp = Chem.rdMolDescriptors.GetMorganFingerprint(mol, 2) 144 | fps = fp.GetNonzeroElements() 145 | score1 = 0. 146 | nf = 0 147 | # for bitId, v in fps.items(): 148 | for bitId, v in fps.items(): 149 | nf += v 150 | sfp = bitId 151 | score1 += SA_model.get(sfp, -4) * v 152 | score1 /= nf 153 | 154 | # features score 155 | nAtoms = mol.GetNumAtoms() 156 | nChiralCenters = len(Chem.FindMolChiralCenters( 157 | mol, includeUnassigned=True)) 158 | ri = mol.GetRingInfo() 159 | nSpiro = Chem.rdMolDescriptors.CalcNumSpiroAtoms(mol) 160 | nBridgeheads = Chem.rdMolDescriptors.CalcNumBridgeheadAtoms(mol) 161 | nMacrocycles = 0 162 | for x in ri.AtomRings(): 163 | if len(x) > 8: 164 | nMacrocycles += 1 165 | 166 | sizePenalty = nAtoms ** 1.005 - nAtoms 167 | stereoPenalty = math.log10(nChiralCenters + 1) 168 | spiroPenalty = math.log10(nSpiro + 1) 169 | bridgePenalty = math.log10(nBridgeheads + 1) 170 | macrocyclePenalty = 0. 171 | 172 | # --------------------------------------- 173 | # This differs from the paper, which defines: 174 | # macrocyclePenalty = math.log10(nMacrocycles+1) 175 | # This form generates better results when 2 or more macrocycles are present 176 | if nMacrocycles > 0: 177 | macrocyclePenalty = math.log10(2) 178 | 179 | score2 = 0. - sizePenalty - stereoPenalty - \ 180 | spiroPenalty - bridgePenalty - macrocyclePenalty 181 | 182 | # correction for the fingerprint density 183 | # not in the original publication, added in version 1.1 184 | # to make highly symmetrical molecules easier to synthetise 185 | score3 = 0. 186 | if nAtoms > len(fps): 187 | score3 = math.log(float(nAtoms) / len(fps)) * .5 188 | 189 | sascore = score1 + score2 + score3 190 | 191 | # need to transform "raw" value into scale between 1 and 10 192 | min = -4.0 193 | max = 2.5 194 | sascore = 11. - (sascore - min + 1) / (max - min) * 9. 195 | # smooth the 10-end 196 | if sascore > 8.: 197 | sascore = 8. + math.log(sascore + 1. - 9.) 198 | if sascore > 10.: 199 | sascore = 10.0 200 | elif sascore < 1.: 201 | sascore = 1.0 202 | 203 | return sascore 204 | 205 | @staticmethod 206 | def synthetic_accessibility_score_scores(mols, norm=False): 207 | scores = [MolecularMetrics._compute_SAS(mol) if mol is not None else None for mol in mols] 208 | scores = np.array(list(map(lambda x: 10 if x is None else x, scores))) 209 | scores = np.clip(MolecularMetrics.remap(scores, 5, 1.5), 0.0, 1.0) if norm else scores 210 | 211 | return scores 212 | 213 | @staticmethod 214 | def diversity_scores(mols, data): 215 | rand_mols = np.random.choice(data.data, 100) 216 | fps = [Chem.rdMolDescriptors.GetMorganFingerprintAsBitVect(mol, 4, nBits=2048) for mol in rand_mols] 217 | 218 | scores = np.array( 219 | list(map(lambda x: MolecularMetrics.__compute_diversity(x, fps) if x is not None else 0, mols))) 220 | scores = np.clip(MolecularMetrics.remap(scores, 0.9, 0.945), 0.0, 1.0) 221 | 222 | return scores 223 | 224 | @staticmethod 225 | def __compute_diversity(mol, fps): 226 | ref_fps = Chem.rdMolDescriptors.GetMorganFingerprintAsBitVect(mol, 4, nBits=2048) 227 | dist = DataStructs.BulkTanimotoSimilarity(ref_fps, fps, returnDistance=True) 228 | score = np.mean(dist) 229 | return score 230 | 231 | @staticmethod 232 | def drugcandidate_scores(mols, data): 233 | 234 | scores = (MolecularMetrics.constant_bump( 235 | MolecularMetrics.water_octanol_partition_coefficient_scores(mols, norm=True), 0.210, 236 | 0.945) + MolecularMetrics.synthetic_accessibility_score_scores(mols, 237 | norm=True) + MolecularMetrics.novel_scores( 238 | mols, data) + (1 - MolecularMetrics.novel_scores(mols, data)) * 0.3) / 4 239 | 240 | return scores 241 | 242 | @staticmethod 243 | def constant_bump(x, x_low, x_high, decay=0.025): 244 | return np.select(condlist=[x <= x_low, x >= x_high], 245 | choicelist=[np.exp(- (x - x_low) ** 2 / decay), 246 | np.exp(- (x - x_high) ** 2 / decay)], 247 | default=np.ones_like(x)) 248 | 249 | 250 | def quantitative_estimation_druglikeness_scores(mols, norm=False): 251 | return np.array(list(map(lambda x: 0 if x is None else x, [ 252 | MolecularMetrics._avoid_sanitization_error(lambda: QED.qed(mol)) if mol is not None else None for mol in 253 | mols]))) -------------------------------------------------------------------------------- /utils/sascorer.py: -------------------------------------------------------------------------------- 1 | # 2 | # calculation of synthetic accessibility score as described in: 3 | # 4 | # Estimation of Synthetic Accessibility Score of Drug-like Molecules based on Molecular Complexity and Fragment Contributions 5 | # Peter Ertl and Ansgar Schuffenhauer 6 | # Journal of Cheminformatics 1:8 (2009) 7 | # http://www.jcheminf.com/content/1/1/8 8 | # 9 | # several small modifications to the original paper are included 10 | # particularly slightly different formula for marocyclic penalty 11 | # and taking into account also molecule symmetry (fingerprint density) 12 | # 13 | # for a set of 10k diverse molecules the agreement between the original method 14 | # as implemented in PipelinePilot and this implementation is r2 = 0.97 15 | # 16 | # peter ertl & greg landrum, september 2013 17 | # 18 | from __future__ import print_function 19 | 20 | from rdkit import Chem 21 | from rdkit.Chem import rdMolDescriptors 22 | # from rdkit.six.moves import cPickle 23 | import pickle as cPickle 24 | from rdkit.six import iteritems 25 | 26 | import math 27 | from collections import defaultdict 28 | 29 | import os.path as op 30 | 31 | _fscores = None 32 | 33 | 34 | def readFragmentScores(name='fpscores'): 35 | import gzip 36 | global _fscores 37 | # generate the full path filename: 38 | if name == "fpscores": 39 | name = op.join(op.dirname(__file__), name) 40 | _fscores = cPickle.load(gzip.open('%s.pkl.gz' % name)) 41 | outDict = {} 42 | for i in _fscores: 43 | for j in range(1, len(i)): 44 | outDict[i[j]] = float(i[0]) 45 | _fscores = outDict 46 | 47 | 48 | def numBridgeheadsAndSpiro(mol, ri=None): 49 | nSpiro = rdMolDescriptors.CalcNumSpiroAtoms(mol) 50 | nBridgehead = rdMolDescriptors.CalcNumBridgeheadAtoms(mol) 51 | return nBridgehead, nSpiro 52 | 53 | 54 | def calculateScore(m): 55 | if _fscores is None: 56 | readFragmentScores() 57 | 58 | # fragment score 59 | fp = rdMolDescriptors.GetMorganFingerprint(m, 2) #<- 2 is the *radius* of the circular fingerprint 60 | fps = fp.GetNonzeroElements() 61 | score1 = 0. 62 | nf = 0 63 | for bitId, v in iteritems(fps): 64 | nf += v 65 | sfp = bitId 66 | score1 += _fscores.get(sfp, -4) * v 67 | score1 /= nf 68 | 69 | # features score 70 | nAtoms = m.GetNumAtoms() 71 | nChiralCenters = len(Chem.FindMolChiralCenters(m, includeUnassigned=True)) 72 | ri = m.GetRingInfo() 73 | nBridgeheads, nSpiro = numBridgeheadsAndSpiro(m, ri) 74 | nMacrocycles = 0 75 | for x in ri.AtomRings(): 76 | if len(x) > 8: 77 | nMacrocycles += 1 78 | 79 | sizePenalty = nAtoms**1.005 - nAtoms 80 | stereoPenalty = math.log10(nChiralCenters + 1) 81 | spiroPenalty = math.log10(nSpiro + 1) 82 | bridgePenalty = math.log10(nBridgeheads + 1) 83 | macrocyclePenalty = 0. 84 | # --------------------------------------- 85 | # This differs from the paper, which defines: 86 | # macrocyclePenalty = math.log10(nMacrocycles+1) 87 | # This form generates better results when 2 or more macrocycles are present 88 | if nMacrocycles > 0: 89 | macrocyclePenalty = math.log10(2) 90 | 91 | score2 = 0. - sizePenalty - stereoPenalty - spiroPenalty - bridgePenalty - macrocyclePenalty 92 | 93 | # correction for the fingerprint density 94 | # not in the original publication, added in version 1.1 95 | # to make highly symmetrical molecules easier to synthetise 96 | score3 = 0. 97 | if nAtoms > len(fps): 98 | score3 = math.log(float(nAtoms) / len(fps)) * .5 99 | 100 | sascore = score1 + score2 + score3 101 | 102 | # need to transform "raw" value into scale between 1 and 10 103 | min = -4.0 104 | max = 2.5 105 | sascore = 11. - (sascore - min + 1) / (max - min) * 9. 106 | # smooth the 10-end 107 | if sascore > 8.: 108 | sascore = 8. + math.log(sascore + 1. - 9.) 109 | if sascore > 10.: 110 | sascore = 10.0 111 | elif sascore < 1.: 112 | sascore = 1.0 113 | 114 | return sascore 115 | 116 | 117 | def processMols(mols): 118 | print('smiles\tName\tsa_score') 119 | for i, m in enumerate(mols): 120 | if m is None: 121 | continue 122 | 123 | s = calculateScore(m) 124 | 125 | smiles = Chem.MolToSmiles(m) 126 | print(smiles + "\t" + m.GetProp('_Name') + "\t%3f" % s) 127 | 128 | 129 | if __name__ == '__main__': 130 | import sys, time 131 | 132 | t1 = time.time() 133 | readFragmentScores("fpscores") 134 | t2 = time.time() 135 | 136 | suppl = Chem.SmilesMolSupplier(sys.argv[1]) 137 | t3 = time.time() 138 | processMols(suppl) 139 | t4 = time.time() 140 | 141 | print('Reading took %.2f seconds. Calculating took %.2f seconds' % ((t2 - t1), (t4 - t3)), 142 | file=sys.stderr) 143 | 144 | # 145 | # Copyright (c) 2013, Novartis Institutes for BioMedical Research Inc. 146 | # All rights reserved. 147 | # 148 | # Redistribution and use in source and binary forms, with or without 149 | # modification, are permitted provided that the following conditions are 150 | # met: 151 | # 152 | # * Redistributions of source code must retain the above copyright 153 | # notice, this list of conditions and the following disclaimer. 154 | # * Redistributions in binary form must reproduce the above 155 | # copyright notice, this list of conditions and the following 156 | # disclaimer in the documentation and/or other materials provided 157 | # with the distribution. 158 | # * Neither the name of Novartis Institutes for BioMedical Research Inc. 159 | # nor the names of its contributors may be used to endorse or promote 160 | # products derived from this software without specific prior written permission. 161 | # 162 | # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 163 | # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 164 | # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 165 | # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 166 | # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 167 | # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 168 | # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 169 | # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 170 | # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 171 | # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 172 | # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 173 | # --------------------------------------------------------------------------------