我们提出了一种分子超图卷积网络(MolHGCN)，利用原子和官能团信息作为输入，预测分子的分子属性。分子可以包含多种类型的官能团，这些官能团会影响分子的性质。例如，一个分子的毒性与毒物团有关，如硝基芳香基团和硫脲。传统的基于图的方法考虑了节点间的成对交互，不能灵活地表达图中节点间的复杂关系，使用多跳可能导致过平滑和过拟合问题。因此，我们提出MolHGCN利用原子和官能团信息来捕获分子之间的亚结构差异。MolHGCN使用来自输入SMILES字符串的功能组信息构造分子的超图表示，使用两阶段消息传递过程(原子和功能组消息传递)提取隐藏表示，并使用提取的隐藏表示预测分子的属性。我们使用Tox21、ClinTox、SIDER、BBBP、BACE、ESOL、FreeSolv和亲脂性数据集评估我们的模型的性能。我们证明了我们的模型能够在大多数数据集上优于其他基线方法。我们特别指出，结合官能团信息和原子信息，可以在潜在空间中获得更好的可分性，从而提高分子性质预测的预测精度。
原文题目：A Hypergraph Convolutional Neural Network for Molecular Properties Prediction using Functional Group
原文：We propose a Molecular Hypergraph Convolutional Network (MolHGCN) that predicts the molecular properties of a molecule using the atom and functional group information as inputs. Molecules can contain many types of functional groups, which will affect the properties the molecules. For example, the toxicity of a molecule is associated with toxicophores, such as nitroaromatic groups and thiourea. Conventional graph-based methods that consider the pair-wise interactions between nodes are inefficient in expressing the complex relationship between multiple nodes in a graph flexibly, and applying multi-hops may result in oversmoothing and overfitting problems. Hence, we propose MolHGCN to capture the substructural difference between molecules using the atom and functional group information. MolHGCN constructs a hypergraph representation of a molecule using functional group information from the input SMILES strings, extracts hidden representation using a two-stage message passing process (atom and functional group message passing), and predicts the properties of the molecules using the extracted hidden representation. We evaluate the performance of our model using Tox21, ClinTox, SIDER, BBBP, BACE, ESOL, FreeSolv and Lipophilicity datasets. We show that our model is able to outperform other baseline methods for most of the datasets. We particularly show that incorporating functional group information along with atom information results in better separability in the latent space, thus increasing the prediction accuracy of the molecule property prediction.
基于功能群的分子性质预测超图卷积神经网络.pdf