DGL & RDKit | 基于Attentive FP的分子性质线性模型

%matplotlib inline 
import matplotlib.pyplot as plt
import os
from rdkit import Chem
from rdkit import RDPaths

import dgl
import numpy as np
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from dgl import model_zoo

from dgl.data.chem.utils import mol_to_complete_graph, mol_to_bigraph

from dgl.data.chem.utils import atom_type_one_hot
from dgl.data.chem.utils import atom_degree_one_hot
from dgl.data.chem.utils import atom_formal_charge
from dgl.data.chem.utils import atom_num_radical_electrons
from dgl.data.chem.utils import atom_hybridization_one_hot
from dgl.data.chem.utils import atom_total_num_H_one_hot
from dgl.data.chem.utils import one_hot_encoding
from dgl.data.chem import CanonicalAtomFeaturizer
from dgl.data.chem import CanonicalBondFeaturizer
from dgl.data.chem import ConcatFeaturizer
from dgl.data.chem import BaseAtomFeaturizer
from dgl.data.chem import BaseBondFeaturizer

from dgl.data.chem import one_hot_encoding
from dgl.data.utils import split_dataset

from functools import partial
from sklearn.metrics import roc_auc_score

def chirality(atom):
    try:
        return one_hot_encoding(atom.GetProp('_CIPCode'), ['R', 'S']) + \
               [atom.HasProp('_ChiralityPossible')]
    except:
        return [False, False] + [atom.HasProp('_ChiralityPossible')]

def collate_molgraphs(data):
    """Batching a list of datapoints for dataloader.
    Parameters
    ----------
    data : list of 3-tuples or 4-tuples.
        Each tuple is for a single datapoint, consisting of
        a SMILES, a DGLGraph, all-task labels and optionally
        a binary mask indicating the existence of labels.
    Returns
    -------
    smiles : list
        List of smiles
    bg : BatchedDGLGraph
        Batched DGLGraphs
    labels : Tensor of dtype float32 and shape (B, T)
        Batched datapoint labels. B is len(data) and
        T is the number of total tasks.
    masks : Tensor of dtype float32 and shape (B, T)
        Batched datapoint binary mask, indicating the
        existence of labels. If binary masks are not
        provided, return a tensor with ones.
    """
    assert len(data[0]) in [3, 4], \
        'Expect the tuple to be of length 3 or 4, got {:d}'.format(len(data[0]))
    if len(data[0]) == 3:
        smiles, graphs, labels = map(list, zip(*data))
        masks = None
    else:
        smiles, graphs, labels, masks = map(list, zip(*data))

    bg = dgl.batch(graphs)
    bg.set_n_initializer(dgl.init.zero_initializer)
    bg.set_e_initializer(dgl.init.zero_initializer)
    labels = torch.stack(labels, dim=0)

    if masks is None:
        masks = torch.ones(labels.shape)
    else:
        masks = torch.stack(masks, dim=0)
    return smiles, bg, labels, masks


def run_a_train_epoch(n_epochs, epoch, model, data_loader,loss_criterion, optimizer):
    model.train()
    total_loss = 0
    losses = []

    for batch_id, batch_data in enumerate(data_loader):
        batch_data
        smiles, bg, labels, masks = batch_data
        if torch.cuda.is_available():
            bg.to(torch.device('cuda:0'))
            labels = labels.to('cuda:0')
            masks = masks.to('cuda:0')

        prediction = model(bg, bg.ndata['hv'], bg.edata['he'])
        loss = (loss_criterion(prediction, labels)*(masks != 0).float()).mean()
        #loss = loss_criterion(prediction, labels)
        #print(loss.shape)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        losses.append(loss.data.item())


    total_score = np.mean(losses)
    print('epoch {:d}/{:d}, training {:.4f}'.format( epoch + 1, n_epochs,  total_score))
    return total_score

atom_featurizer = BaseAtomFeaturizer(
                 {'hv': ConcatFeaturizer([
                  partial(atom_type_one_hot, allowable_set=[
                          'B', 'C', 'N', 'O', 'F', 'Si', 'P', 'S', 'Cl', 'As', 'Se', 'Br', 'Te', 'I', 'At'],
                    encode_unknown=True),
                  partial(atom_degree_one_hot, allowable_set=list(range(6))),
                  atom_formal_charge, atom_num_radical_electrons,
                  partial(atom_hybridization_one_hot, encode_unknown=True),
                  lambda atom: [0], # A placeholder for aromatic information,
                    atom_total_num_H_one_hot, chirality
                 ],
                )})
bond_featurizer = BaseBondFeaturizer({
                                     'he': lambda bond: [0 for _ in range(10)]
    })

train_mols = Chem.SDMolSupplier('solubility.train.sdf')
train_smi =[Chem.MolToSmiles(m) for m in train_mols]
train_sol = torch.tensor([float(mol.GetProp('SOL')) for mol in train_mols]).reshape(-1,1)
 
test_mols =  Chem.SDMolSupplier('solubility.test.sdf')
test_smi = [Chem.MolToSmiles(m) for m in test_mols]
test_sol = torch.tensor([float(mol.GetProp('SOL')) for mol in test_mols]).reshape(-1,1)
 
 
train_graph =[mol_to_bigraph(mol,
                           node_featurizer=atom_featurizer, 
                           edge_featurizer=bond_featurizer) for mol in train_mols]
 
test_graph =[mol_to_bigraph(mol,
                           node_featurizer=atom_featurizer, 
                           edge_featurizer=bond_featurizer) for mol in test_mols]

model = model_zoo.chem.AttentiveFP(node_feat_size=39,
                                  edge_feat_size=10,
                                  num_layers=2,
                                  num_timesteps=2,
                                  graph_feat_size=200,
                                  output_size=1,
                                  dropout=0.2)
 
train_loader = DataLoader(dataset=list(zip(train_smi, train_graph, train_sol)), batch_size=128, collate_fn=collate_molgraphs)
test_loader = DataLoader(dataset=list(zip(test_smi, test_graph, test_sol)), batch_size=128, collate_fn=collate_molgraphs)

loss_fn = nn.MSELoss(reduction='none')
optimizer = torch.optim.Adam(model.parameters(), lr=10 ** (-2.5), weight_decay=10 ** (-5.0),)
n_epochs = 100
epochs = []
scores = []
for e in range(n_epochs):
    score = run_a_train_epoch(n_epochs, e, model, train_loader, loss_fn, optimizer)
    epochs.append(e)
    scores.append(score)
 
plt.plot(epochs, scores)

from sklearn.metrics import r2_score
print(r2_score(test_sol, res))