Wang Conghao, Kumar Gaurav Asok, Rajapakse Jagath C
College of Computing and Data Science, Nanyang Technological University, Singapore, 639798, Singapore.
Sci Rep. 2025 Jan 2;15(1):179. doi: 10.1038/s41598-024-83090-3.
Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. However, existing methods mainly focus on forward encoding of drugs, which is to obtain an accurate prediction of the response levels, but omitted to decipher the reaction mechanism between drug molecules and genes. We propose the eXplainable Graph-based Drug response Prediction (XGDP) approach that achieves a precise drug response prediction and reveals the comprehensive mechanism of action between drugs and their targets. XGDP represents drugs with molecular graphs, which naturally preserve the structural information of molecules and a Graph Neural Network module is applied to learn the latent features of molecules. Gene expression data from cancer cell lines are incorporated and processed by a Convolutional Neural Network module. A couple of deep learning attribution algorithms are leveraged to interpret interactions between drug molecular features and genes. We demonstrate that XGDP not only enhances the prediction accuracy compared to pioneering works but is also capable of capturing the salient functional groups of drugs and interactions with significant genes of cancer cells.
理解药物作用机制对于药物反应预测和精准医学至关重要。机器学习和深度学习算法的空前发展加速了药物反应预测研究。然而，现有方法主要集中于药物的正向编码，即获得反应水平的准确预测，但忽略了解析药物分子与基因之间的反应机制。我们提出了基于可解释图的药物反应预测（XGDP）方法，该方法实现了精确的药物反应预测，并揭示了药物与其靶点之间的综合作用机制。XGDP用分子图表示药物，分子图自然地保留了分子的结构信息，并应用图神经网络模块来学习分子的潜在特征。来自癌细胞系的基因表达数据由卷积神经网络模块进行整合和处理。利用几种深度学习归因算法来解释药物分子特征与基因之间的相互作用。我们证明，与开创性工作相比，XGDP不仅提高了预测准确性，而且能够捕捉药物的显著官能团以及与癌细胞重要基因的相互作用。
