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)