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from rdkit import Chem
from rdkit.Chem import AllChem
import numpy as np
import torch
from Model.GCN import mol2graph
def get_data(data_path):
mols, labels = [], []
with open(data_path, 'r') as f:
smis = f.read().splitlines()
for smi in smis:
smi = smi.split(' ')
labels.append(int(smi[0].strip('[]')))
smi = smi[1:]
smi = ''.join(smi)
mols.append(Chem.MolFromSmiles(smi))
return np.array(mols), np.array(labels)
"""
get_neg_sample: select negative sample according to the frequent distribution of library.
Correct fragments(y) and fragments couldn't be connected to target(y_mask) are masked. """
@torch.no_grad()
def get_neg_sample(freq, y):
# y: (batch_size, )
# freq: (1, ), frequency of templates
batch_size = y.size(0)
freq = freq.repeat(batch_size, 1)
freq.scatter_(1, y.unsqueeze(1), 0)
neg_idxs = torch.multinomial(freq, 1, True).view(-1)
return neg_idxs
def template_prediction(GCN_model, input_smi, num_sampling, GCN_device=None):
mol = Chem.MolFromSmiles(input_smi)
data = mol2graph.mol2vec(mol).to(GCN_device)
with torch.no_grad():
output = GCN_model.forward(data.x, data.edge_index, data.batch).squeeze() # shape(1, 1000) -> (1000,)
try:
_, indices = torch.topk(output, num_sampling)
except:
indices = None
return indices
def batch_template_prediction(GCN_model, input_smi, num_sampling=5, GCN_device=None):
mol = Chem.MolFromSmiles(input_smi)
data = mol2graph.mol2vec(mol).to(GCN_device)
output = GCN_model.forward(data.x, data.edge_index, data.batch).squeeze() # shape(1, 1000) -> (1000,)
_, indices = torch.topk(output, num_sampling)
return indices
def check_templates(indices, input_smi, r_dict):
matched_indices = []
molecule = Chem.MolFromSmiles(input_smi)
for i in indices:
idx = str(i.item())
rsmi = r_dict[idx]
rxn = AllChem.ReactionFromSmarts(rsmi)
reactants = rxn.GetReactants()
flag = False
for reactant in reactants:
if molecule.HasSubstructMatch(reactant):
flag = True
if flag == True:
matched_indices.append(f'[{i.item()}]')
return matched_indices # list of string, ex) ['[0]', '[123]', ... '[742]']
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