Ma Xuhao, Wu Tingfang, Li Geng, Wang Junkai, Jiang Yelu, Quan Lijun, Lyu Qiang
School of Computer Science and Technology, Soochow University, Ganjiang East Streat 333, 215006, Jiangsu, China.
School of Computer Science and Technology, Soochow University, Ganjiang East Streat 333, 215006, Jiangsu, China; Province Key Lab for Information Processing Technologies, Soochow University, Ganjiang East Streat 333, 215006, Jiangsu, China; Collaborative Innovation Center of Novel Software Technology and Industrialization, Organization, Nanjing 210000, Jiangsu, China.
J Mol Biol. 2025 Mar 15;437(6):168916. doi: 10.1016/j.jmb.2024.168916. Epub 2024 Dec 16.
Evaluating the frequencies of drug-side effects is crucial in drug development and risk-benefit analysis. While existing deep learning methods show promise, they have yet to explore using heterogeneous networks to simultaneously model the various relationship between drugs and side effects, highlighting areas for potential enhancement. In this study, we propose DSE-HNGCN, a novel method that leverages heterogeneous networks to simultaneously model the various relationships between drugs and side effects. By employing multi-layer graph convolutional networks, we aim to mine the interactions between drugs and side effects to predict the frequencies of drug-side effects. To address the over-smoothing problem in graph convolutional networks and capture diverse semantic information from different layers, we introduce a layer importance combination strategy. Additionally, we have developed an integrated prediction module that effectively utilizes drug and side effect features from different networks. Our experimental results, using benchmark datasets in a range of scenarios, show that our model outperforms existing methods in predicting the frequencies of drug-side effects. Comparative experiments and visual analysis highlight the substantial benefits of incorporating heterogeneous networks and other pertinent modules, thus improving the accuracy of DSE-HNGCN predictions. We also provide interpretability for DSE-HNGCN, indicating that the extracted features are potentially biologically significant. Case studies validate our model's capability to identify potential side effects of drugs, offering valuable insights for subsequent biological validation experiments.
评估药物副作用的频率在药物研发和风险效益分析中至关重要。虽然现有的深度学习方法显示出了前景，但它们尚未探索使用异构网络来同时对药物和副作用之间的各种关系进行建模，这突出了潜在的改进领域。在本研究中，我们提出了DSE-HNGCN，这是一种利用异构网络同时对药物和副作用之间的各种关系进行建模的新方法。通过采用多层图卷积网络，我们旨在挖掘药物和副作用之间的相互作用，以预测药物副作用的频率。为了解决图卷积网络中的过平滑问题并从不同层捕获多样的语义信息，我们引入了一种层重要性组合策略。此外，我们开发了一个集成预测模块，该模块有效地利用了来自不同网络的药物和副作用特征。我们在一系列场景中使用基准数据集进行的实验结果表明，我们的模型在预测药物副作用频率方面优于现有方法。对比实验和可视化分析突出了纳入异构网络和其他相关模块的显著优势，从而提高了DSE-HNGCN预测的准确性。我们还为DSE-HNGCN提供了解释性，表明提取的特征可能具有生物学意义。案例研究验证了我们的模型识别药物潜在副作用的能力，为后续的生物学验证实验提供了有价值的见解。
