DGL & RDKit | 基于Attentive FP可视化训练模型原子权重
【摘要】 DGL具有许多用于化学信息学、药物与生物信息学任务的函数。
DGL开发人员提供了用于可视化训练模型原子权重的代码。使用Attentive FP构建模型后，可以可视化给定分子的原子权重，意味着每个原子对目标值的贡献量。
基于Attentive FP可视化训练模型原子权重
环境准备
PyTorch：深度学习框架DGL：基于PyTorch的库，支持深度学习以处理图形RDK...
DGL具有许多用于化学信息学、药物与生物信息学任务的函数。
DGL开发人员提供了用于可视化训练模型原子权重的代码。使用Attentive FP构建模型后，可以可视化给定分子的原子权重，意味着每个原子对目标值的贡献量。
基于Attentive FP可视化训练模型原子权重
环境准备
- PyTorch：深度学习框架
- DGL：基于PyTorch的库，支持深度学习以处理图形
- RDKit：用于构建分子图并从字符串表示形式绘制结构式
- MDTraj：用于分子动力学轨迹分析的开源库
导入库
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%matplotlib inline
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import matplotlib.pyplot as plt
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import os
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from rdkit import Chem
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from rdkit import RDPaths
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import dgl
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import numpy as np
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import random
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.data import DataLoader
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from torch.utils.data import Dataset
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from dgl import model_zoo
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from dgl.data.chem.utils import mol_to_complete_graph, mol_to_bigraph
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from dgl.data.chem.utils import atom_type_one_hot
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from dgl.data.chem.utils import atom_degree_one_hot
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from dgl.data.chem.utils import atom_formal_charge
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from dgl.data.chem.utils import atom_num_radical_electrons
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from dgl.data.chem.utils import atom_hybridization_one_hot
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from dgl.data.chem.utils import atom_total_num_H_one_hot
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from dgl.data.chem.utils import one_hot_encoding
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from dgl.data.chem import CanonicalAtomFeaturizer
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from dgl.data.chem import CanonicalBondFeaturizer
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from dgl.data.chem import ConcatFeaturizer
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from dgl.data.chem import BaseAtomFeaturizer
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from dgl.data.chem import BaseBondFeaturizer
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from dgl.data.chem import one_hot_encoding
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from dgl.data.utils import split_dataset
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from functools import partial
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from sklearn.metrics import roc_auc_score
代码来源于dgl/example
DGL开发人员提供了用于可视化训练模型原子权重的代码。
使用Attentive FP构建模型后，可以可视化给定分子的原子权重，意味着每个原子对目标值的贡献量。
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def chirality(atom):
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try:
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return one_hot_encoding(atom.GetProp('_CIPCode'), ['R', 'S']) + \
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[atom.HasProp('_ChiralityPossible')]
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except:
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return [False, False] + [atom.HasProp('_ChiralityPossible')]
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def collate_molgraphs(data):
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"""Batching a list of datapoints for dataloader.
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Parameters
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----------
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data : list of 3-tuples or 4-tuples.
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Each tuple is for a single datapoint, consisting of
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a SMILES, a DGLGraph, all-task labels and optionally
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a binary mask indicating the existence of labels.
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Returns
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-------
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smiles : list
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List of smiles
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bg : BatchedDGLGraph
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Batched DGLGraphs
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labels : Tensor of dtype float32 and shape (B, T)
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Batched datapoint labels. B is len(data) and
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T is the number of total tasks.
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masks : Tensor of dtype float32 and shape (B, T)
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Batched datapoint binary mask, indicating the
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existence of labels. If binary masks are not
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provided, return a tensor with ones.
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"""
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assert len(data[0]) in [3, 4], \
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'Expect the tuple to be of length 3 or 4, got {:d}'.format(len(data[0]))
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if len(data[0]) == 3:
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smiles, graphs, labels = map(list, zip(*data))
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masks = None
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else:
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smiles, graphs, labels, masks = map(list, zip(*data))
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bg = dgl.batch(graphs)
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bg.set_n_initializer(dgl.init.zero_initializer)
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bg.set_e_initializer(dgl.init.zero_initializer)
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labels = torch.stack(labels, dim=0)
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if masks is None:
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masks = torch.ones(labels.shape)
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else:
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masks = torch.stack(masks, dim=0)
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return smiles, bg, labels, masks
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atom_featurizer = BaseAtomFeaturizer(
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{'hv': ConcatFeaturizer([
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partial(atom_type_one_hot, allowable_set=[
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'B', 'C', 'N', 'O', 'F', 'Si', 'P', 'S', 'Cl', 'As', 'Se', 'Br', 'Te', 'I', 'At'],
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encode_unknown=True),
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partial(atom_degree_one_hot, allowable_set=list(range(6))),
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atom_formal_charge, atom_num_radical_electrons,
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partial(atom_hybridization_one_hot, encode_unknown=True),
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lambda atom: [0], # A placeholder for aromatic information,
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atom_total_num_H_one_hot, chirality
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],
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)})
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bond_featurizer = BaseBondFeaturizer({
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'he': lambda bond: [0 for _ in range(10)]
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})
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train_mols = Chem.SDMolSupplier('solubility.train.sdf')
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train_smi =[Chem.MolToSmiles(m) for m in train_mols]
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train_sol = torch.tensor([float(mol.GetProp('SOL')) for mol in train_mols]).reshape(-1,1)
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test_mols = Chem.SDMolSupplier('solubility.test.sdf')
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test_smi = [Chem.MolToSmiles(m) for m in test_mols]
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test_sol = torch.tensor([float(mol.GetProp('SOL')) for mol in test_mols]).reshape(-1,1)
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train_graph =[mol_to_bigraph(mol,
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node_featurizer=atom_featurizer,
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edge_featurizer=bond_featurizer) for mol in train_mols]
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test_graph =[mol_to_bigraph(mol,
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node_featurizer=atom_featurizer,
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edge_featurizer=bond_featurizer) for mol in test_mols]
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def run_a_train_epoch(n_epochs, epoch, model, data_loader,loss_criterion, optimizer):
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model.train()
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total_loss = 0
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losses = []
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for batch_id, batch_data in enumerate(data_loader):
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batch_data
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smiles, bg, labels, masks = batch_data
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if torch.cuda.is_available():
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bg.to(torch.device('cuda:0'))
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labels = labels.to('cuda:0')
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masks = masks.to('cuda:0')
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prediction = model(bg, bg.ndata['hv'], bg.edata['he'])
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loss = (loss_criterion(prediction, labels)*(masks != 0).float()).mean()
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#loss = loss_criterion(prediction, labels)
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#print(loss.shape)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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losses.append(loss.data.item())
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#total_score = np.mean(train_meter.compute_metric('rmse'))
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total_score = np.mean(losses)
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print('epoch {:d}/{:d}, training {:.4f}'.format( epoch + 1, n_epochs, total_score))
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return total_score
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model = model_zoo.chem.AttentiveFP(node_feat_size=39,
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edge_feat_size=10,
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num_layers=2,
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num_timesteps=2,
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graph_feat_size=200,
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output_size=1,
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dropout=0.2)
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train_loader = DataLoader(dataset=list(zip(train_smi, train_graph, train_sol)), batch_size=128, collate_fn=collate_molgraphs)
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test_loader = DataLoader(dataset=list(zip(test_smi, test_graph, test_sol)), batch_size=128, collate_fn=collate_molgraphs)
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loss_fn = nn.MSELoss(reduction='none')
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optimizer = torch.optim.Adam(model.parameters(), lr=10 ** (-2.5), weight_decay=10 ** (-5.0),)
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n_epochs = 100
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epochs = []
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scores = []
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for e in range(n_epochs):
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score = run_a_train_epoch(n_epochs, e, model, train_loader, loss_fn, optimizer)
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epochs.append(e)
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scores.append(score)
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model.eval()
导入用于分子可视化依赖库
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import copy
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from rdkit.Chem import rdDepictor
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from rdkit.Chem.Draw import rdMolDraw2D
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from IPython.display import SVG
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from IPython.display import display
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import matplotlib
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import matplotlib.cm as cm
定义可视化函数
- 代码来源于DGL库。
- DGL模型具有get_node_weight选项，该选项返回图形的node_weight。该模型具有两层GRU，因此以下代码我将0用作时间步长，因此时间步长必须为0或1。
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def drawmol(idx, dataset, timestep):
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smiles, graph, _ = dataset[idx]
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print(smiles)
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bg = dgl.batch([graph])
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atom_feats, bond_feats = bg.ndata['hv'], bg.edata['he']
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if torch.cuda.is_available():
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print('use cuda')
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bg.to(torch.device('cuda:0'))
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atom_feats = atom_feats.to('cuda:0')
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bond_feats = bond_feats.to('cuda:0')
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_, atom_weights = model(bg, atom_feats, bond_feats, get_node_weight=True)
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assert timestep < len(atom_weights), 'Unexpected id for the readout round'
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atom_weights = atom_weights[timestep]
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min_value = torch.min(atom_weights)
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max_value = torch.max(atom_weights)
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atom_weights = (atom_weights - min_value) / (max_value - min_value)
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norm = matplotlib.colors.Normalize(vmin=0, vmax=1.28)
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cmap = cm.get_cmap('bwr')
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plt_colors = cm.ScalarMappable(norm=norm, cmap=cmap)
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atom_colors = {i: plt_colors.to_rgba(atom_weights[i].data.item()) for i in range(bg.number_of_nodes())}
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mol = Chem.MolFromSmiles(smiles)
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rdDepictor.Compute2DCoords(mol)
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drawer = rdMolDraw2D.MolDraw2DSVG(280, 280)
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drawer.SetFontSize(1)
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op = drawer.drawOptions()
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mol = rdMolDraw2D.PrepareMolForDrawing(mol)
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drawer.DrawMolecule(mol, highlightAtoms=range(bg.number_of_nodes()),
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highlightBonds=[],
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highlightAtomColors=atom_colors)
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drawer.FinishDrawing()
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svg = drawer.GetDrawingText()
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svg = svg.replace('svg:', '')
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if torch.cuda.is_available():
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atom_weights = atom_weights.to('cpu')
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return (Chem.MolFromSmiles(smiles), atom_weights.data.numpy(), svg)
绘制测试数据集分子
该模型预测溶解度，颜色表示红色是溶解度的积极影响，蓝色是负面影响。
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target = test_loader.dataset
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for i in range(len(target)):
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mol, aw, svg = drawmol(i, target, 0)
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display(SVG(svg))
。。。。。
参考资料
1. https://github.com/dmlc/dgl/tree/master/apps/life_sci
2. https://github.com/dmlc/dgl/blob/master/python/dgl/model_zoo/chem/attentive_fp.py
3. https://pubs.acs.org/doi/full/10.1021/acs.jcim.9b00387
文章来源: drugai.blog.csdn.net，作者：DrugAI，版权归原作者所有，如需转载，请联系作者。
原文链接：drugai.blog.csdn.net/article/details/104868996
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