Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.
Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
Biomolecules. 2024 Feb 21;14(3):253. doi: 10.3390/biom14030253.
Drug combination therapy shows promise in cancer treatment by addressing drug resistance, reducing toxicity, and enhancing therapeutic efficacy. However, the intricate and dynamic nature of biological systems makes identifying potential synergistic drugs a costly and time-consuming endeavor. To facilitate the development of combination therapy, techniques employing artificial intelligence have emerged as a transformative solution, providing a sophisticated avenue for advancing existing therapeutic approaches. In this study, we developed SynerGNet, a graph neural network model designed to accurately predict the synergistic effect of drug pairs against cancer cell lines. SynerGNet utilizes cancer-specific featured graphs created by integrating heterogeneous biological features into the human protein-protein interaction network, followed by a reduction process to enhance topological diversity. Leveraging synergy data provided by AZ-DREAM Challenges, the model yields a balanced accuracy of 0.68, significantly outperforming traditional machine learning. Encouragingly, augmenting the training data with carefully constructed synthetic instances improved the balanced accuracy of SynerGNet to 0.73. Finally, the results of an independent validation conducted against DrugCombDB demonstrated that it exhibits a strong performance when applied to unseen data. SynerGNet shows a great potential in detecting drug synergy, positioning itself as a valuable tool that could contribute to the advancement of combination therapy for cancer treatment.
药物联合治疗通过解决药物耐药性、降低毒性和提高治疗效果，在癌症治疗方面显示出巨大的潜力。然而，生物系统的复杂和动态性质使得确定潜在的协同药物成为一项昂贵且耗时的工作。为了促进联合治疗的发展，采用人工智能的技术已经成为一种变革性的解决方案，为现有治疗方法的推进提供了一个复杂的途径。在这项研究中，我们开发了 SynerGNet，这是一个图神经网络模型，旨在准确预测药物对 against 癌细胞系的协同作用。SynerGNet 利用通过将异质生物学特征整合到人类蛋白质-蛋白质相互作用网络中创建的癌症特异性特征图，并通过减少过程来增强拓扑多样性。利用来自 AZ-DREAM 挑战赛的协同数据，该模型的平衡准确率为 0.68，明显优于传统的机器学习。令人鼓舞的是，通过仔细构建的合成实例来增加训练数据，将 SynerGNet 的平衡准确率提高到了 0.73。最后，对 DrugCombDB 进行的独立验证的结果表明，它在应用于未见数据时表现出强大的性能。SynerGNet 在检测药物协同作用方面具有巨大的潜力，它是一种有价值的工具，可以为癌症治疗的联合治疗的推进做出贡献。
