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mol_generator_random.py
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mol_generator_random.py
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# import datetime
# starttime = datetime.datetime.now()
from rdkit.Chem import AllChem
from rdkit import Chem
import torch
import pandas as pd
import numpy as np
from method.net import Net
frag_path = './data/frag_lib.smi'
res_path = './data/mols_rand_prob.csv'
model_path = './model/fruity_stat.pkl'
number_of_structures = 2000
PRED = 0.9
model = Net()
model.load_state_dict(torch.load(model_path))
model.to('cuda:0')
model.eval()
main_molecules = [molecule for molecule in Chem.SmilesMolSupplier(frag_path, delimiter='\t', titleLine=False)
if molecule is not None]
fragment_molecules = [molecule for molecule in Chem.SmilesMolSupplier(frag_path, delimiter='\t', titleLine=False)
if molecule is not None]
# fragment_molecules = [molecule for molecule in Chem.SDMolSupplier('fragments.sdf') if molecule is not None]
bond_list = [Chem.rdchem.BondType.UNSPECIFIED, Chem.rdchem.BondType.SINGLE, Chem.rdchem.BondType.DOUBLE,
Chem.rdchem.BondType.TRIPLE, Chem.rdchem.BondType.QUADRUPLE, Chem.rdchem.BondType.QUINTUPLE,
Chem.rdchem.BondType.HEXTUPLE, Chem.rdchem.BondType.ONEANDAHALF, Chem.rdchem.BondType.TWOANDAHALF,
Chem.rdchem.BondType.THREEANDAHALF, Chem.rdchem.BondType.FOURANDAHALF, Chem.rdchem.BondType.FIVEANDAHALF,
Chem.rdchem.BondType.AROMATIC, Chem.rdchem.BondType.IONIC, Chem.rdchem.BondType.HYDROGEN,
Chem.rdchem.BondType.THREECENTER, Chem.rdchem.BondType.DATIVEONE, Chem.rdchem.BondType.DATIVE,
Chem.rdchem.BondType.DATIVEL, Chem.rdchem.BondType.DATIVER, Chem.rdchem.BondType.OTHER,
Chem.rdchem.BondType.ZERO]
prob = []
generated_structures = []
generated_structure_number = 0
while generated_structure_number < number_of_structures:
selected_main_molecule_number = np.floor(
np.random.rand(1) * len(main_molecules)).astype(int)[0]
main_molecule = main_molecules[selected_main_molecule_number]
# make adjacency matrix and get atoms for main molecule
main_adjacency_matrix = Chem.rdmolops.GetAdjacencyMatrix(main_molecule)
for bond in main_molecule.GetBonds():
main_adjacency_matrix[bond.GetBeginAtomIdx(
), bond.GetEndAtomIdx()] = bond_list.index(bond.GetBondType())
main_adjacency_matrix[bond.GetEndAtomIdx(
), bond.GetBeginAtomIdx()] = bond_list.index(bond.GetBondType())
main_atoms = []
for atom in main_molecule.GetAtoms():
main_atoms.append(atom.GetSymbol())
r_index_in_main_molecule_old = [
index for index, atom in enumerate(main_atoms) if atom == '*']
for index, r_index in enumerate(r_index_in_main_molecule_old):
modified_index = r_index - index
atom = main_atoms.pop(modified_index)
main_atoms.append(atom)
tmp = main_adjacency_matrix[:,
modified_index:modified_index + 1].copy()
main_adjacency_matrix = np.delete(
main_adjacency_matrix, modified_index, 1)
main_adjacency_matrix = np.c_[main_adjacency_matrix, tmp]
tmp = main_adjacency_matrix[modified_index:modified_index + 1, :].copy()
main_adjacency_matrix = np.delete(
main_adjacency_matrix, modified_index, 0)
main_adjacency_matrix = np.r_[main_adjacency_matrix, tmp]
r_index_in_main_molecule_new = [
index for index, atom in enumerate(main_atoms) if atom == '*']
r_bonded_atom_index_in_main_molecule = []
for number in r_index_in_main_molecule_new:
r_bonded_atom_index_in_main_molecule.append(
np.where(main_adjacency_matrix[number, :] != 0)[0][0])
r_bond_number_in_main_molecule = main_adjacency_matrix[
r_index_in_main_molecule_new, r_bonded_atom_index_in_main_molecule]
main_adjacency_matrix = np.delete(
main_adjacency_matrix, r_index_in_main_molecule_new, 0)
main_adjacency_matrix = np.delete(
main_adjacency_matrix, r_index_in_main_molecule_new, 1)
for i in range(len(r_index_in_main_molecule_new)):
main_atoms.remove('*')
main_size = main_adjacency_matrix.shape[0]
selected_fragment_numbers = np.floor(np.random.rand(
len(r_index_in_main_molecule_old)) * len(fragment_molecules)).astype(int)
generated_molecule_atoms = main_atoms[:]
generated_adjacency_matrix = main_adjacency_matrix.copy()
for r_number_in_molecule in range(len(r_index_in_main_molecule_new)):
fragment_molecule = fragment_molecules[selected_fragment_numbers[r_number_in_molecule]]
fragment_adjacency_matrix = Chem.rdmolops.GetAdjacencyMatrix(
fragment_molecule)
for bond in fragment_molecule.GetBonds():
fragment_adjacency_matrix[bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()] = bond_list.index(
bond.GetBondType())
fragment_adjacency_matrix[bond.GetEndAtomIdx(), bond.GetBeginAtomIdx()] = bond_list.index(
bond.GetBondType())
fragment_atoms = []
for atom in fragment_molecule.GetAtoms():
fragment_atoms.append(atom.GetSymbol())
# integrate adjacency matrix
r_index_in_fragment_molecule = fragment_atoms.index('*')
r_bonded_atom_index_in_fragment_molecule = \
np.where(fragment_adjacency_matrix[r_index_in_fragment_molecule, :] != 0)[
0][0]
if r_bonded_atom_index_in_fragment_molecule > r_index_in_fragment_molecule:
r_bonded_atom_index_in_fragment_molecule -= 1
fragment_atoms.remove('*')
fragment_adjacency_matrix = np.delete(
fragment_adjacency_matrix, r_index_in_fragment_molecule, 0)
fragment_adjacency_matrix = np.delete(
fragment_adjacency_matrix, r_index_in_fragment_molecule, 1)
main_size = generated_adjacency_matrix.shape[0]
generated_adjacency_matrix = np.c_[generated_adjacency_matrix, np.zeros(
[generated_adjacency_matrix.shape[0], fragment_adjacency_matrix.shape[0]], dtype='int32')]
generated_adjacency_matrix = np.r_[generated_adjacency_matrix, np.zeros(
[fragment_adjacency_matrix.shape[0], generated_adjacency_matrix.shape[1]], dtype='int32')]
generated_adjacency_matrix[r_bonded_atom_index_in_main_molecule[
r_number_in_molecule], r_bonded_atom_index_in_fragment_molecule + main_size] = \
r_bond_number_in_main_molecule[r_number_in_molecule]
generated_adjacency_matrix[
r_bonded_atom_index_in_fragment_molecule + main_size, r_bonded_atom_index_in_main_molecule[
r_number_in_molecule]] = r_bond_number_in_main_molecule[r_number_in_molecule]
generated_adjacency_matrix[main_size:,
main_size:] = fragment_adjacency_matrix
# integrate atoms
generated_molecule_atoms += fragment_atoms
# generate structures
generated_molecule = Chem.RWMol()
atom_index = []
for atom_number in range(len(generated_molecule_atoms)):
atom = Chem.Atom(generated_molecule_atoms[atom_number])
molecular_index = generated_molecule.AddAtom(atom)
atom_index.append(molecular_index)
for index_x, row_vector in enumerate(generated_adjacency_matrix):
for index_y, bond in enumerate(row_vector):
if index_y <= index_x:
continue
if bond == 0:
continue
else:
generated_molecule.AddBond(
atom_index[index_x], atom_index[index_y], bond_list[bond])
generated_molecule = generated_molecule.GetMol()
# generated_molecule = generated_molecule.updatePropertyCache()
generated_molecule_smiles = Chem.MolToSmiles(generated_molecule)
generated_mol = Chem.MolFromSmiles(generated_molecule_smiles)
if not generated_mol:
continue
gene_mole_fp = AllChem.GetMorganFingerprintAsBitVect(generated_mol, 2)
X = torch.FloatTensor(gene_mole_fp).cuda()
X = X.unsqueeze(0)
pred = model(X).sigmoid_().cpu().detach().numpy()[0][0]
# filter: if prediction >= PRED, then output.
if pred >= PRED and generated_molecule_smiles not in generated_structures:
prob.append(np.around(pred, 4))
generated_structures.append(generated_molecule_smiles)
generated_structure_number += 1
if (generated_structure_number + 1) % 100 == 0 or (generated_structure_number + 1) == number_of_structures:
print(generated_structure_number + 1, '/', number_of_structures)
else:
continue
mols_df = pd.DataFrame(generated_structures)
prob_df = pd.DataFrame(prob)
res_df = pd.concat([mols_df, prob_df], axis=1)
res_df.columns = ['smiles', 'prob']
ref_df = res_df.sort_values(by='prob', ascending=False)
ref_df.index = [i for i in range(number_of_structures)]
id_df = pd.DataFrame([i for i in range(1, number_of_structures + 1)])
res_fin = pd.concat([id_df, ref_df], axis=1)
res_fin.columns = ['id', 'smiles', 'proba']
res_fin.to_csv(res_path, index=None)
# endtime = datetime.datetime.now()
# print((endtime - starttime).seconds)