targets = SA_scores_normalized + qed_values_normalized + cycle_scores_normalized reg_scores = [] # collect y-values for objective function qed_scores = [] # collect scores for qed term in objective function SA_values = [] for i in range(len(valid_smiles_final)): to_add = [] qed_store = [] SA = [] if len(valid_smiles_final[i]) != 0: for j in range(0, len(valid_smiles_final[i])): current_qed_value = qed.default( MolFromSmiles(valid_smiles_final[i][j])) current_SA_score = -sascorer.calculateScore( MolFromSmiles(valid_smiles_final[i][j])) cycle_list = nx.cycle_basis( nx.Graph( rdmolops.GetAdjacencyMatrix( MolFromSmiles(valid_smiles_final[i][j])))) if len(cycle_list) == 0: cycle_length = 0 else: cycle_length = max([len(j) for j in cycle_list]) if cycle_length <= 6: cycle_length = 0 else: cycle_length = cycle_length - 6 current_cycle_score = -cycle_length
enc_dec = lasp.EncoderDecoder() encoder, decoder = enc_dec.get_functions() smiles_rdkit = [] for i in range(len(smiles)): smiles_rdkit.append(MolToSmiles(MolFromSmiles(smiles[i]))) print(i) logP_values = [] for i in range(len(smiles)): logP_values.append(Descriptors.MolLogP(MolFromSmiles(smiles_rdkit[i]))) print(i) SA_scores = [] for i in range(len(smiles)): SA_scores.append(-sascorer.calculateScore(MolFromSmiles(smiles_rdkit[i]))) print(i) cycle_scores = [] for i in range(len(smiles)): cycle_list = nx.cycle_basis(nx.Graph(rdmolops.GetAdjacencyMatrix(MolFromSmiles(smiles_rdkit[i])))) if len(cycle_list) == 0: cycle_length = 0 else: cycle_length = max([len(j) for j in cycle_list]) if cycle_length <= 6: cycle_length = 0 else: cycle_length = cycle_length - 6 cycle_scores.append(-cycle_length) print(i)