Gu Yuexi, Zu Jian, Sun Yongheng, Zhang Louxin
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China.
Department of Mathematics and Centre for Data Science and Machine Learning, National University of Singapore, Singapore 119076, Singapore.
Bioinformatics. 2025 Jul 1;41(Supplement_1):i86-i95. doi: 10.1093/bioinformatics/btaf215.
Drug combinations can not only enhance drug efficacy but also effectively reduce toxic side effects and mitigate drug resistance. With the advancement of drug combination screening technologies, large amounts of data have been generated. The availability of large data enables researchers to develop deep learning methods for predicting drug targets for synergistic combination. However, these methods still lack sufficient accuracy for practical use, and most overlook the biological significance of their models.
We propose the HIG-Syn (hypergraph and interaction-aware multigranularity network for drug synergy prediction) model, which integrates a coarse-granularity module and a fine-granularity module to predict drug combination synergy. The former utilizes a hypergraph to capture global features, while the latter employs interaction-aware attention to simulate biological processes by modeling substructure-substructure and substructure-cell line interactions. HIG-Syn outperforms state-of-the-art machine learning models on our validation datasets extracted from the DrugComb and GDSC2 databases. Furthermore, the fact that five of the 12 novel synergistic drug combinations predicted by HIG-Syn are strongly supported by experimental evidence in the literature underscores its practical potential.
The source code is available at https://github.com/gracygyx/HIGSyn.
药物组合不仅可以提高药物疗效，还能有效降低毒副作用并减轻耐药性。随着药物组合筛选技术的进步，已产生了大量数据。大数据的可用性使研究人员能够开发深度学习方法来预测协同组合的药物靶点。然而，这些方法在实际应用中仍缺乏足够的准确性，并且大多数方法忽略了其模型的生物学意义。
我们提出了HIG-Syn（用于药物协同作用预测的超图和交互感知多粒度网络）模型，该模型集成了粗粒度模块和细粒度模块来预测药物组合的协同作用。前者利用超图捕获全局特征，而后者采用交互感知注意力通过对亚结构-亚结构和亚结构-细胞系相互作用进行建模来模拟生物学过程。在我们从DrugComb和GDSC2数据库中提取的验证数据集上，HIG-Syn的性能优于当前最先进的机器学习模型。此外，HIG-Syn预测的12种新型协同药物组合中有5种得到了文献中实验证据的有力支持，这突出了其实际应用潜力。
