Predicción de la solubilidad molecular utilizando redes neuronales convolucionales gráficas

class DatasetsHolder:
    @staticmethod
    def read_datasets(inp_folder_path):
        df = pd.read_csv(inp_folder_path)
        return df
        # return pandas DataFrame

class StandardizeDatasets:
    @staticmethod
    def standardize_smiles(smi: str) -> Optional[str]:
        mol = Chem.MolFromSmiles(smi)
        mol = Chem.MolToSmiles(mol)
        return mol
        "crete typical standardization of one smiles"

    @logger.catch()
    def standardize(self, inp_path: Path, out_path: Path):
        df_reader = DatasetsHolder()
        df = df_reader.read_datasets(inp_path)
        with Pool(10) as pool:
          df['standardize_smiles'] = list(
                      tqdm(pool.imap(self.standardize_smiles, df.smiles), total=df.shape[0])
                  )
        df.to_csv(out_path, index=False)
        return df
        "apply standardization to all smiles"

class StandardizeTautomers(StandardizeDatasets):
    @staticmethod
    def standardize_smiles(smi: str) -> Optional[str]:
        Canonicalizer = TautomerCanonicalizer()
        mol = Chem.MolFromSmiles(smi)
        standorized = Canonicalizer.canonicalize(mol)
        return Chem.MolToSmiles(standorized)
      # "apply TautomerCanonicalizer() to standardization"
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

from descriptastorus.descriptors import rdDescriptors
from rdkit import Chem
import logging
from descriptastorus.descriptors import rdNormalizedDescriptors

generator = rdNormalizedDescriptors.RDKit2DNormalized()

def rdkit_2d_features(smiles: str):
    features = generator.process(smiles)
    if features[0] == False:
        print(f'{smiles} were not processed correctly')
        return None
    else:
        return features[1:]

def create_feature_dataframe(df):
    feature_names = [x[0] for x in generator.columns]

    rdkit_feats = [ ]
    for i in range(len(df)):
        smiles = df.iloc[i][SMILES_COLUMN]
        target_value = df.iloc[i]['measured log solubility in mols per litre']
        features = generator.process(smiles)
        dictionary = dict(zip(feature_names, features[1:]))
        dictionary['target'] = target_value
        rdkit_feats.append(dictionary)

    return pd.DataFrame(rdkit_feats)
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

from xgboost import XGBRegressor
X_train = train_data.drop(columns=['target'])
y_train = train_data['target']
X_test = test_data.drop(columns=['target'])
y_test = test_data['target']
model = XGBRegressor()
model.fit(X_train, y_train)
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

def get_atom_features(mol):
   atomic_number = []
   num_hs = []
   degrees = []
   charges = []
   tags = []
   hybridizations = []
   aromatic = []
   mass = []
 
   for atom in mol.GetAtoms():
       atomic_number.append(atom.GetAtomicNum()) # atomic number
       num_hs.append(atom.GetTotalNumHs(includeNeighbors=True)) # number of H in atom
       degrees.append(atom.GetTotalDegree()) # total Degree of atom
       charges.append(atom.GetFormalCharge()) # Charge of atom
       tags.append(int(atom.GetChiralTag())) # chiral tag
       hybridizations.append(int(atom.GetHybridization())) # hybridization of atom
       if atom.GetIsAromatic(): # Is aromatic of not
           aromatic.append(1)
       else:
           aromatic.append(0)
       mass.append(atom.GetMass() * 0.01) # mass
              
   return torch.tensor([atomic_number, num_hs, degrees, charges, tags, hybridizations, aromatic, mass]).t()
 
def get_edge_index(mol):
   row, col = [], []
  
   for bond in mol.GetBonds():
       start, end = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
       row += [start, end]
       col += [end, start]
      
   return torch.tensor([row, col], dtype=torch.long)
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

class GCNConv(MessagePassing):
   def __init__(self, in_channels, out_channels):
       super(GCNConv, self).__init__(aggr='add')  # "Add" aggregation (Step 5).
       self.lin = torch.nn.Linear(in_channels, out_channels)
 
   def forward(self, x, edge_index):
       # x has shape [N, in_channels]
       # edge_index has shape [2, E]
 
       # Step 1: Add self-loops to the adjacency matrix.
       edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
 
       # Step 2: Linearly transform node feature matrix.
       x = self.lin(x)
 
       # Step 3: Compute normalization.
       row, col = edge_index
       deg = degree(col, x.size(0), dtype=x.dtype)
       deg_inv_sqrt = deg.pow(-0.5)
       norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]
 
       # Step 4-5: Start propagating messages.
       return self.propagate(edge_index, x=x, norm=norm)
 
   def message(self, x_j, norm):
       # x_j has shape [E, out_channels]
 
       # Step 4: Normalize node features.
       return norm.view(-1, 1) * x_j
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

class NeuralFP(nn.Module):
   def __init__(self, atom_features=52, fp_size=50):
       super(NeuralFP, self).__init__()
      
       self.atom_features = atom_features
       self.fp_size = fp_size
 
      
       self.loop1 =  GCNConv(atom_features, fp_size)
 
       self.loops = nn.ModuleList([self.loop1])
      
   def forward(self, data):
       fingerprint = torch.zeros((data.batch.shape[0], self.fp_size), dtype=torch.float).to(device)
      
       out = data.x
       for idx, loop in enumerate(self.loops):
    
           updated_fingerprint = loop(out, data.edge_index)
           fingerprint += updated_fingerprint
 
       return scatter_add(fingerprint, data.batch, dim=0)
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

import torch.nn.functional as F
 
class MLP_Regressor(nn.Module):
   def __init__(self, neural_fp, atom_features=2, fp_size=50, hidden_size=100):
       super(MLP_Regressor, self).__init__()
 
 
       self.neural_fp = neural_fp
       self.lin1 =  nn.Linear(fp_size , hidden_size)
       self.leakyrelu = nn.LeakyReLU(0.2)
       self.lin2 =  nn.Linear(hidden_size, 1)
       self.dropout =  nn.Dropout(0.2)
  
   def forward(self, batch):
 
       fp = self.neural_fp(batch)
       hidden =  self.dropout(self.lin1(fp))
       out =  self.leakyrelu(self.lin2(hidden))
       return out
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

class MLP_Regressor(nn.Module):
   def __init__(self, neural_fp, atom_features=2, fp_size=100, hidden_size=300, num_additional_features = 207):
       super(MLP_Regressor, self).__init__()
 
       self.neural_fp = neural_fp
       self.lin1 =  nn.Linear(fp_size+num_additional_features, hidden_size)
       self.leakyrelu = nn.LeakyReLU(0.2)
       self.lin2 =  nn.Linear(hidden_size, 1)
       self.dropout =  nn.Dropout(0.2)
  
   def forward(self, batch, additional_features):
 
       fp = self.neural_fp(batch)
       fp = torch.cat((fp, additional_features), dim=1)
       hidden = self.dropout(self.lin1(fp))
       out = self.leakyrelu(self.lin2(hidden))
       return out