412 lines
17 KiB
Python
412 lines
17 KiB
Python
from rdkit import Chem, RDLogger
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RDLogger.DisableLog('rdApp.*')
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from fcd_torch import FCD as FCDMetric
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from mini_moses.metrics.metrics import FragMetric, internal_diversity
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from mini_moses.metrics.utils import get_mol, mapper
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import re
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import time
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import random
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random.seed(0)
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import numpy as np
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from multiprocessing import Pool
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import torch
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from metrics.property_metric import calculateSAS
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bond_dict = [None, Chem.rdchem.BondType.SINGLE, Chem.rdchem.BondType.DOUBLE, Chem.rdchem.BondType.TRIPLE,
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Chem.rdchem.BondType.AROMATIC]
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ATOM_VALENCY = {6: 4, 7: 3, 8: 2, 9: 1, 15: 3, 16: 2, 17: 1, 35: 1, 53: 1}
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bd_dict_x = {'O2-N2': [5.00E+04, 1.00E-03]}
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bd_dict_y = {'O2-N2': [5.00E+04/2.78E+04, 1.00E-03/2.43E-05]}
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selectivity = ['O2-N2']
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a_dict = {}
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b_dict = {}
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for prop_name in selectivity:
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x1, x2 = np.log10(bd_dict_x[prop_name][0]), np.log10(bd_dict_x[prop_name][1])
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y1, y2 = np.log10(bd_dict_y[prop_name][0]), np.log10(bd_dict_y[prop_name][1])
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a = (y1-y2)/(x1-x2)
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b = y1-a*x1
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a_dict[prop_name] = a
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b_dict[prop_name] = b
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def selectivity_evaluation(gas1, gas2, prop_name):
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x = np.log10(np.array(gas1))
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y = np.log10(np.array(gas1) / np.array(gas2))
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upper = (y - (a_dict[prop_name] * x + b_dict[prop_name])) > 0
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return upper
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class BasicMolecularMetrics(object):
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def __init__(self, atom_decoder, train_smiles=None, stat_ref=None, task_evaluator=None, n_jobs=8, device='cpu', batch_size=512):
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self.dataset_smiles_list = train_smiles
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self.atom_decoder = atom_decoder
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self.n_jobs = n_jobs
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self.device = device
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self.batch_size = batch_size
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self.stat_ref = stat_ref
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self.task_evaluator = task_evaluator
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def compute_relaxed_validity(self, generated, ensure_connected):
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valid = []
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num_components = []
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all_smiles = []
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valid_mols = []
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covered_atoms = set()
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direct_valid_count = 0
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for graph in generated:
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atom_types, edge_types = graph
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mol = build_molecule_with_partial_charges(atom_types, edge_types, self.atom_decoder)
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direct_valid_flag = True if check_mol(mol, largest_connected_comp=True) is not None else False
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if direct_valid_flag:
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direct_valid_count += 1
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if not ensure_connected:
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mol_conn, _ = correct_mol(mol, connection=True)
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mol = mol_conn if mol_conn is not None else correct_mol(mol, connection=False)[0]
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else: # ensure fully connected
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mol, _ = correct_mol(mol, connection=True)
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smiles = mol2smiles(mol)
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mol = get_mol(smiles)
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try:
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mol_frags = Chem.rdmolops.GetMolFrags(mol, asMols=True, sanitizeFrags=True)
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num_components.append(len(mol_frags))
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largest_mol = max(mol_frags, default=mol, key=lambda m: m.GetNumAtoms())
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smiles = mol2smiles(largest_mol)
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if smiles is not None and largest_mol is not None and len(smiles) > 1 and Chem.MolFromSmiles(smiles) is not None:
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valid_mols.append(largest_mol)
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valid.append(smiles)
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for atom in largest_mol.GetAtoms():
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covered_atoms.add(atom.GetSymbol())
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all_smiles.append(smiles)
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else:
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all_smiles.append(None)
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except Exception as e:
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# print(f"An error occurred: {e}")
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all_smiles.append(None)
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return valid, len(valid) / len(generated), direct_valid_count / len(generated), np.array(num_components), all_smiles, covered_atoms
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def evaluate(self, generated, targets, ensure_connected, active_atoms=None):
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""" generated: list of pairs (positions: n x 3, atom_types: n [int])
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the positions and atom types should already be masked. """
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valid, validity, nc_validity, num_components, all_smiles, covered_atoms = self.compute_relaxed_validity(generated, ensure_connected=ensure_connected)
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nc_mu = num_components.mean() if len(num_components) > 0 else 0
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nc_min = num_components.min() if len(num_components) > 0 else 0
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nc_max = num_components.max() if len(num_components) > 0 else 0
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len_active = len(active_atoms) if active_atoms is not None else 1
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cover_str = f"Cover {len(covered_atoms)} ({len(covered_atoms)/len_active * 100:.2f}%) atoms: {covered_atoms}"
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print(f"Validity over {len(generated)} molecules: {validity * 100 :.2f}% (w/o correction: {nc_validity * 100 :.2f}%), cover {len(covered_atoms)} ({len(covered_atoms)/len_active * 100:.2f}%) atoms: {covered_atoms}")
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print(f"Number of connected components of {len(generated)} molecules: min:{nc_min:.2f} mean:{nc_mu:.2f} max:{nc_max:.2f}")
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if validity > 0:
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dist_metrics = {'cover_str': cover_str ,'validity': validity, 'validity_nc': nc_validity}
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unique = list(set(valid))
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close_pool = False
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if self.n_jobs != 1:
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pool = Pool(self.n_jobs)
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close_pool = True
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else:
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pool = 1
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valid_mols = mapper(pool)(get_mol, valid)
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dist_metrics['interval_diversity'] = internal_diversity(valid_mols, pool, device=self.device)
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start_time = time.time()
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if self.stat_ref is not None:
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kwargs = {'n_jobs': pool, 'device': self.device, 'batch_size': self.batch_size}
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kwargs_fcd = {'n_jobs': self.n_jobs, 'device': self.device, 'batch_size': self.batch_size}
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try:
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dist_metrics['sim/Frag'] = FragMetric(**kwargs)(gen=valid_mols, pref=self.stat_ref['Frag'])
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except:
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print('error: ', 'pool', pool)
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print('valid_mols: ', valid_mols)
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dist_metrics['dist/FCD'] = FCDMetric(**kwargs_fcd)(gen=valid, pref=self.stat_ref['FCD'])
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if self.task_evaluator is not None:
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evaluation_list = list(self.task_evaluator.keys())
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evaluation_list = evaluation_list.copy()
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assert 'meta_taskname' in evaluation_list
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meta_taskname = self.task_evaluator['meta_taskname']
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evaluation_list.remove('meta_taskname')
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meta_split = meta_taskname.split('-')
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valid_index = np.array([True if smiles else False for smiles in all_smiles])
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targets_log = {}
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for i, name in enumerate(evaluation_list):
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targets_log[f'input_{name}'] = np.array([float('nan')] * len(valid_index))
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targets_log[f'input_{name}'] = targets[:, i]
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targets = targets[valid_index]
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if len(meta_split) == 2:
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cached_perm = {meta_split[0]: None, meta_split[1]: None}
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for i, name in enumerate(evaluation_list):
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if name == 'scs':
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continue
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elif name == 'sas':
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scores = calculateSAS(valid)
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else:
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scores = self.task_evaluator[name](valid)
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targets_log[f'output_{name}'] = np.array([float('nan')] * len(valid_index))
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targets_log[f'output_{name}'][valid_index] = scores
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if name in ['O2', 'N2', 'CO2']:
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if len(meta_split) == 2:
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cached_perm[name] = scores
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scores, cur_targets = np.log10(scores), np.log10(targets[:, i])
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dist_metrics[f'{name}/mae'] = np.mean(np.abs(scores - cur_targets))
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elif name == 'sas':
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dist_metrics[f'{name}/mae'] = np.mean(np.abs(scores - targets[:, i]))
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else:
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true_y = targets[:, i]
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predicted_labels = (scores >= 0.5).astype(int)
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acc = (predicted_labels == true_y).sum() / len(true_y)
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dist_metrics[f'{name}/acc'] = acc
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if len(meta_split) == 2:
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if cached_perm[meta_split[0]] is not None and cached_perm[meta_split[1]] is not None:
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task_name = self.task_evaluator['meta_taskname']
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upper = selectivity_evaluation(cached_perm[meta_split[0]], cached_perm[meta_split[1]], task_name)
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dist_metrics[f'selectivity/{task_name}'] = np.sum(upper)
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end_time = time.time()
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elapsed_time = end_time - start_time
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max_key_length = max(len(key) for key in dist_metrics)
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print(f'Details over {len(valid)} ({len(generated)}) valid (total) molecules, calculating metrics using {elapsed_time:.2f} s:')
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strs = ''
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for i, (key, value) in enumerate(dist_metrics.items()):
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if isinstance(value, (int, float, np.floating, np.integer)):
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strs = strs + f'{key:>{max_key_length}}:{value:<7.4f}\t'
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if i % 4 == 3:
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strs = strs + '\n'
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print(strs)
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if close_pool:
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pool.close()
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pool.join()
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else:
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unique = []
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dist_metrics = {}
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targets_log = None
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return unique, dict(nc_min=nc_min, nc_max=nc_max, nc_mu=nc_mu), all_smiles, dist_metrics, targets_log
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def mol2smiles(mol):
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if mol is None:
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return None
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try:
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Chem.SanitizeMol(mol)
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except ValueError:
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return None
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return Chem.MolToSmiles(mol)
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def build_molecule_with_partial_charges(atom_types, edge_types, atom_decoder, verbose=False):
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if verbose:
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print("\nbuilding new molecule")
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mol = Chem.RWMol()
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for atom in atom_types:
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a = Chem.Atom(atom_decoder[atom.item()])
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mol.AddAtom(a)
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if verbose:
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print("Atom added: ", atom.item(), atom_decoder[atom.item()])
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edge_types = torch.triu(edge_types)
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all_bonds = torch.nonzero(edge_types)
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for i, bond in enumerate(all_bonds):
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if bond[0].item() != bond[1].item():
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mol.AddBond(bond[0].item(), bond[1].item(), bond_dict[edge_types[bond[0], bond[1]].item()])
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if verbose:
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print("bond added:", bond[0].item(), bond[1].item(), edge_types[bond[0], bond[1]].item(),
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bond_dict[edge_types[bond[0], bond[1]].item()])
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# add formal charge to atom: e.g. [O+], [N+], [S+]
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# not support [O-], [N-], [S-], [NH+] etc.
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flag, atomid_valence = check_valency(mol)
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if verbose:
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print("flag, valence", flag, atomid_valence)
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if flag:
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continue
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else:
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if len(atomid_valence) == 2:
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idx = atomid_valence[0]
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v = atomid_valence[1]
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an = mol.GetAtomWithIdx(idx).GetAtomicNum()
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if verbose:
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print("atomic num of atom with a large valence", an)
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if an in (7, 8, 16) and (v - ATOM_VALENCY[an]) == 1:
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mol.GetAtomWithIdx(idx).SetFormalCharge(1)
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# print("Formal charge added")
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else:
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continue
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return mol
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def check_valency(mol):
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try:
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Chem.SanitizeMol(mol, sanitizeOps=Chem.SanitizeFlags.SANITIZE_PROPERTIES)
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return True, None
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except ValueError as e:
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e = str(e)
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p = e.find('#')
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e_sub = e[p:]
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atomid_valence = list(map(int, re.findall(r'\d+', e_sub)))
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return False, atomid_valence
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def correct_mol(mol, connection=False):
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#####
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no_correct = False
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flag, _ = check_valency(mol)
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if flag:
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no_correct = True
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while True:
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if connection:
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mol_conn = connect_fragments(mol)
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# if mol_conn is not None:
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mol = mol_conn
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if mol is None:
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return None, no_correct
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flag, atomid_valence = check_valency(mol)
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if flag:
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break
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else:
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try:
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assert len(atomid_valence) == 2
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idx = atomid_valence[0]
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v = atomid_valence[1]
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queue = []
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check_idx = 0
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for b in mol.GetAtomWithIdx(idx).GetBonds():
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type = int(b.GetBondType())
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queue.append((b.GetIdx(), type, b.GetBeginAtomIdx(), b.GetEndAtomIdx()))
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if type == 12:
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check_idx += 1
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queue.sort(key=lambda tup: tup[1], reverse=True)
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if queue[-1][1] == 12:
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return None, no_correct
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elif len(queue) > 0:
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start = queue[check_idx][2]
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end = queue[check_idx][3]
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t = queue[check_idx][1] - 1
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mol.RemoveBond(start, end)
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if t >= 1:
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mol.AddBond(start, end, bond_dict[t])
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except Exception as e:
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# print(f"An error occurred in correction: {e}")
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return None, no_correct
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return mol, no_correct
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def check_mol(m, largest_connected_comp=True):
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if m is None:
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return None
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sm = Chem.MolToSmiles(m, isomericSmiles=True)
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if largest_connected_comp and '.' in sm:
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vsm = [(s, len(s)) for s in sm.split('.')] # 'C.CC.CCc1ccc(N)cc1CCC=O'.split('.')
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vsm.sort(key=lambda tup: tup[1], reverse=True)
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mol = Chem.MolFromSmiles(vsm[0][0])
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else:
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mol = Chem.MolFromSmiles(sm)
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return mol
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##### connect fragements
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def select_atom_with_available_valency(frag):
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atoms = list(frag.GetAtoms())
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random.shuffle(atoms)
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for atom in atoms:
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if atom.GetAtomicNum() > 1 and atom.GetImplicitValence() > 0:
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return atom
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return None
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def select_atoms_with_available_valency(frag):
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return [atom for atom in frag.GetAtoms() if atom.GetAtomicNum() > 1 and atom.GetImplicitValence() > 0]
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def try_to_connect_fragments(combined_mol, frag, atom1, atom2):
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# Make copies of the molecules to try the connection
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trial_combined_mol = Chem.RWMol(combined_mol)
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trial_frag = Chem.RWMol(frag)
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# Add the new fragment to the combined molecule with new indices
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new_indices = {atom.GetIdx(): trial_combined_mol.AddAtom(atom) for atom in trial_frag.GetAtoms()}
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# Add the bond between the suitable atoms from each fragment
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trial_combined_mol.AddBond(atom1.GetIdx(), new_indices[atom2.GetIdx()], Chem.BondType.SINGLE)
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# Adjust the hydrogen count of the connected atoms
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for atom_idx in [atom1.GetIdx(), new_indices[atom2.GetIdx()]]:
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atom = trial_combined_mol.GetAtomWithIdx(atom_idx)
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num_h = atom.GetTotalNumHs()
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atom.SetNumExplicitHs(max(0, num_h - 1))
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# Add bonds for the new fragment
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for bond in trial_frag.GetBonds():
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trial_combined_mol.AddBond(new_indices[bond.GetBeginAtomIdx()], new_indices[bond.GetEndAtomIdx()], bond.GetBondType())
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# Convert to a Mol object and try to sanitize it
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new_mol = Chem.Mol(trial_combined_mol)
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try:
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Chem.SanitizeMol(new_mol)
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return new_mol # Return the new valid molecule
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except Chem.MolSanitizeException:
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return None # If the molecule is not valid, return None
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def connect_fragments(mol):
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# Get the separate fragments
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frags = Chem.GetMolFrags(mol, asMols=True, sanitizeFrags=False)
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if len(frags) < 2:
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return mol
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combined_mol = Chem.RWMol(frags[0])
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for frag in frags[1:]:
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# Select all atoms with available valency from both molecules
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atoms1 = select_atoms_with_available_valency(combined_mol)
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atoms2 = select_atoms_with_available_valency(frag)
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# Try to connect using all combinations of available valency atoms
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for atom1 in atoms1:
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for atom2 in atoms2:
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new_mol = try_to_connect_fragments(combined_mol, frag, atom1, atom2)
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if new_mol is not None:
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# If a valid connection is made, update the combined molecule and break
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combined_mol = new_mol
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break
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else:
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# Continue if the inner loop didn't break (no valid connection found for atom1)
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continue
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# Break if the inner loop did break (valid connection found)
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break
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else:
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# If no valid connections could be made with any of the atoms, return None
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return None
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return combined_mol
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#### connect fragements
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def compute_molecular_metrics(molecule_list, targets, train_smiles, stat_ref, dataset_info, task_evaluator, comput_config):
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""" molecule_list: (dict) """
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atom_decoder = dataset_info.atom_decoder
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active_atoms = dataset_info.active_atoms
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ensure_connected = dataset_info.ensure_connected
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metrics = BasicMolecularMetrics(atom_decoder, train_smiles, stat_ref, task_evaluator, **comput_config)
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evaluated_res = metrics.evaluate(molecule_list, targets, ensure_connected, active_atoms)
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all_smiles = evaluated_res[-3]
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all_metrics = evaluated_res[-2]
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targets_log = evaluated_res[-1]
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unique_smiles = evaluated_res[0]
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return unique_smiles, all_smiles, all_metrics, targets_log
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if __name__ == '__main__':
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smiles_mol = 'C1CCC1'
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print("Smiles mol %s" % smiles_mol)
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chem_mol = Chem.MolFromSmiles(smiles_mol)
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print(block_mol)
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