Liu Shuo, Chen Mengyun, Yao Xiaojun, Liu Huanxiang
School of Pharmacy, Lanzhou University, Lanzhou, 730000, China.
Huawei Technologies Co., Ltd., Hangzhou, 310000, China.
J Pharm Anal. 2025 Jun;15(6):101242. doi: 10.1016/j.jpha.2025.101242. Epub 2025 Feb 20.
Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials. Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction. However, traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules. Similarly, graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information. To address these limitations, we propose a novel fingerprint-enhanced hierarchical graph neural network (FH-GNN) for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints. The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks (D-MPNN) on a hierarchical molecular graph that integrates atomic-level, motif-level, and graph-level information along with their relationships. Additionally, we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features, creating a comprehensive molecular embedding that integrated hierarchical molecular structures with domain knowledge. Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction, validating its capability to comprehensively capture molecular information. By integrating molecular structure and chemical knowledge, FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.
准确预测分子性质对于筛选具有理想性质的化合物以及降低试验成本和风险至关重要。基于手工构建特征的传统方法和基于图的方法在分子性质预测方面已显示出有前景的结果。然而，传统方法依赖专家知识，并且常常无法捕捉分子内部的复杂结构和相互作用。同样，基于图的方法通常会忽略隐藏在分子基序中的化学结构和功能，并且难以有效地整合全局和局部分子信息。为了解决这些局限性，我们提出了一种用于分子性质预测的新型指纹增强分层图神经网络（FH-GNN），它同时从分层分子图和指纹中学习信息。FH-GNN通过在一个分层分子图上应用定向消息传递神经网络（D-MPNN）来捕捉多样的分层化学信息，该分层分子图整合了原子级、基序级和图级信息及其关系。此外，我们使用了一种自适应注意力机制来平衡分层图和指纹特征的重要性，创建了一个将分层分子结构与领域知识相结合的综合分子嵌入。在来自MoleculeNet的八个基准数据集上进行的实验表明，FH-GNN在分子性质预测的分类和回归任务中均优于基线模型，验证了其全面捕捉分子信息的能力。通过整合分子结构和化学知识，FH-GNN为准确预测分子性质提供了一个强大的工具，并有助于发现潜在的药物候选物。
