将分子SMILES生成DGLGraph

其实将分子的SMILES转化为图是很简单的，也是很便捷的，主要有以下几步：

一、导入相关的包

import numpy as np
import torch
import dgl
from dgl import DGLGraph
from rdkit import Chem
from rdkit.Chem import rdMolDescriptors as rdDesc

二、将SMILES转化为RDKIT的mol对象，同时生成一个空的dgl图

molecule_smiles='[C@@H](Cl)(F)Br'
G = DGLGraph()
#加载smile生成mol对象
molecule = Chem.MolFromSmiles(molecule_smiles)

三、为DGL图添加节点

G.add_nodes(molecule.GetNumAtoms())

四、提取原子的特征和化学键特征

get_atom_features和get_bond_features分别为提取原子特征和化学键特征的函数，如下：

这里先插入一个辅助函数：

#辅助函数
def one_of_k_encoding_unk(x, allowable_set):
    '将x与allowable_set逐个比较，相同为True， 不同为False, 都不同则认为是最后一个相同'
    if x not in allowable_set:
        x = allowable_set[-1]
    return list(map(lambda s: x == s, allowable_set))

获取原子的特征，特征包括：元素种类、隐含价、价电子、成键、电荷、杂化类型

def get_atom_features(atom):
    possible_atom = ['C', 'N', 'O', 'F', 'P', 'Cl', 'Br', 'I', 'DU'] #DU代表其他原子
    atom_features = one_of_k_encoding_unk(atom.GetSymbol(), possible_atom)
    atom_features += one_of_k_encoding_unk(atom.GetImplicitValence(), [0, 1])
    atom_features += one_of_k_encoding_unk(atom.GetNumRadicalElectrons(), [0, 1])
    atom_features += one_of_k_encoding_unk(atom.GetDegree(), [0, 1, 2, 3, 4, 5, 6])
    atom_features += one_of_k_encoding_unk(atom.GetFormalCharge(), [-1, 1])
    atom_features += one_of_k_encoding_unk(atom.GetHybridization(), 
                                           [Chem.rdchem.HybridizationType.SP, 
                                            Chem.rdchem.HybridizationType.SP2,
                                            Chem.rdchem.HybridizationType.SP3, 
                                            Chem.rdchem.HybridizationType.SP3D])       
    return np.array(atom_features) 

获取边特征，包括：是否为单键、双键、三键、成环、芳香环、共轭

def get_bond_features(bond):
    bond_type = bond.GetBondType()
    bond_feats = [
        bond_type == Chem.rdchem.BondType.SINGLE, bond_type == Chem.rdchem.BondType.DOUBLE,
        bond_type == Chem.rdchem.BondType.TRIPLE, bond_type == Chem.rdchem.BondType.AROMATIC,
        bond.GetIsConjugated(),
        bond.IsInRing()
    ]
    return np.array(bond_feats)

五、提取每一个原子的特征（节点特征和边特征）生成图

node_features = []
edge_features = []

for i in range(molecule.GetNumAtoms()):
    atom_i = molecule.GetAtomWithIdx(i) 
    atom_i_features = get_atom_features(atom_i) 
    node_features.append(atom_i_features)
    
    for j in range(molecule.GetNumAtoms()):
        bond_ij = molecule.GetBondBetweenAtoms(i, j)
        if bond_ij is not None:
            G.add_edges(i,j) 
            bond_features_ij = get_bond_features(bond_ij) 
            edge_features.append(bond_features_ij)

G.ndata['x'] = torch.from_numpy(np.array(node_features)) #dgl添加原子/节点特征
G.edata['w'] = torch.from_numpy(np.array(edge_features)) #dgl添加键/边特征
G

六、输出结果为：