文献类型：期刊文献

英文题名：XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures

作者：Wang, Yimeng[1];Yu, Xinxin[1];Gu, Yaxin[1];Li, Weihua[1];Zhu, Keyun[1];Chen, Long[1];Tang, Yun[1];Liu, Guixia[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2024

卷号：168

外文期刊名：COMPUTERS IN BIOLOGY AND MEDICINE

收录：;EI(收录号：20234915160128);WOS:【SCI-EXPANDED(收录号:WOS:001127032300001)】；

基金：The current research was supported by financial support from the National Key Research and Development Program of China (Grant 2019YFA0904800) , the National Natural Science Foundation of China (Grants 82173746, 82273858, and 82273930) , and Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission) .

语种：英文

外文关键词：Cancer drug sensitivity prediction; Deep learning; Gene pathways; Chemical structures; Interpretation

摘要：Cancer is a highly complex disease characterized by genetic and phenotypic heterogeneity among individuals. In the era of precision medicine, understanding the genetic basis of these individual differences is crucial for developing new drugs and achieving personalized treatment. Despite the increasing abundance of cancer genomics data, predicting the relationship between cancer samples and drug sensitivity remains challenging. In this study, we developed an explainable graph neural network framework for predicting cancer drug sensitivity (XGraphCDS) based on comparative learning by integrating cancer gene expression information and drug chemical structure knowledge. Specifically, XGraphCDS consists of a unified heterogeneous network and multiple sub-networks, with molecular graphs representing drugs and gene enrichment scores representing cell lines. Experimental results showed that XGraphCDS consistently outperformed most state-of-the-art baselines (R2 = 0.863, AUC = 0.858). We also constructed a separate in vivo prediction model by using transfer learning strategies with in vitro experimental data and achieved good predictive power (AUC = 0.808). Simultaneously, our framework is interpretable, providing insights into resistance mechanisms alongside accurate predictions. The excellent performance of XGraphCDS highlights its immense potential in aiding the development of selective anti-tumor drugs and personalized dosing strategies in the field of precision medicine.