Molecular-graph-BERT 代码实现

Molecular-graph-BERT 是一种基于图神经网络的化学分子表示方法，可用于分子性质预测、分子设计等应用。以下是 Molecular-graph-BERT 的代码实现。

1. 安装依赖

!pip install torch
!pip install dgl
!pip install rdkit

2. 数据预处理

import dgl
from rdkit import Chem
from dgl.data.utils import load_graphs, save_graphs
from dgl.data.chem.utils import smiles_to_bigraph, CanonicalAtomFeaturizer

# 将 SMILES 序列转换为 DGLGraph
def graph_from_smiles(smiles):
    mol = Chem.MolFromSmiles(smiles)
    return smiles_to_bigraph(smiles, atom_featurizer=CanonicalAtomFeaturizer())

# 读取数据，并将 SMILES 序列转换为 DGLGraph
data = []
with open('data.txt', 'r') as f:
    for line in f:
        smiles, label = line.strip().split('\t')
        g = graph_from_smiles(smiles)
        label = int(label)
        data.append((g, label))

# 将 DGLGraph 序列化并保存为二进制文件
save_graphs('data.bin', data)

3. 定义模型

import torch
import torch.nn as nn
import dgl.function as fn

# 定义 GraphConvLayer
class GraphConvLayer(nn.Module):
    def __init__(self, in_feats, out_feats):
        super(GraphConvLayer, self).__init__()
        self.linear = nn.Linear(in_feats, out_feats)
        self.activation = nn.ReLU()

    def forward(self, g, features):
        with g.local_scope():
            g.ndata['h'] = features
            g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'neigh'))
            h_neigh = g.ndata['neigh']
            h = self.linear(features + h_neigh)
            h = self.activation(h)
            return h

# 定义 MolecularGraphBERT 模型
class MolecularGraphBERT(nn.Module):
    def __init__(self, hidden_size, num_layers):
        super(MolecularGraphBERT, self).__init__()
        self.embed = nn.Embedding(100, hidden_size)
        self.layers = nn.ModuleList([GraphConvLayer(hidden_size, hidden_size) for _ in range(num_layers)])
        self.pool = dgl.nn.pytorch.glob.max_pool

    def forward(self, g):
        h = self.embed(g.ndata['feat'])
        for layer in self.layers:
            h = layer(g, h)
        g.ndata['h'] = h
        hg = self.pool(g, g.ndata['h'])
        return hg

4. 训练模型

from torch.utils.data import DataLoader
from dgl.data.utils import load_graphs

# 加载数据
data, _ = load_graphs('data.bin')
labels = torch.tensor([d[1] for d in data])

# 划分训练集和测试集
train_data, test_data = data[:80], data[80:]
train_labels, test_labels = labels[:80], labels[80:]

# 定义训练参数
lr = 0.01
num_epochs = 50
hidden_size = 128
num_layers = 3

# 定义模型和优化器
model = MolecularGraphBERT(hidden_size, num_layers)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)

# 训练模型
for epoch in range(num_epochs):
    model.train()
    for i, (g, label) in enumerate(train_data):
        pred = model(g)
        loss = nn.functional.binary_cross_entropy_with_logits(pred, label.unsqueeze(0).float())
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    model.eval()
    with torch.no_grad():
        train_acc = 0
        for g, label in train_data:
            pred = model(g)
            train_acc += ((pred > 0).long() == label).sum().item()
        train_acc /= len(train_data)

        test_acc = 0
        for g, label in test_data:
            pred = model(g)
            test_acc += ((pred > 0).long() == label).sum().item()
        test_acc /= len(test_data)

    print('Epoch {:d} | Train Acc {:.4f} | Test Acc {:.4f}'.format(epoch, train_acc, test_acc))

以上就是 Molecular-graph-BERT 的代码实现。需要注意的是，由于 Molecular-graph-BERT 是基于图神经网络的方法，需要使用 DGL 库来构建和操作图数据，因此需要先安装 DGL 库。