SGCLMD: Signed graph-based contrastive learning model for predicting somatic mutation-drug association.

Somatic mutations could influence critical cellular processes, leading to uncontrolled cell growth and tumor formation. Understanding the intricate interactions between somatic mutations and drugs was crucial for advancing our knowledge of the underlying biological mechanisms of cancer. This knowledge, in turn, could drive advancements in cancer detection, diagnosis, and treatment. Exploring the relationships between specific somatic mutations and drug responses held the potential to identify targeted therapeutic interventions and improve personalized treatment strategies for cancer patients. In this study, we introduced a computational model, the signed graph comparison learning for mutation-drug associations (SGCLMD), designed to predict signs of somatic mutation-drug associations. Initially, we leveraged clinical data to construct a benchmark dataset encompassing somatic mutation-drug associations. We proposed a graph enhancement method, employing a random perturbation strategy, to expand the signed graph. This approach not only preserved interaction information across the two perspectives of the signed graph but also retained implicit relationships between these perspectives. Furthermore, we devised a multi-view comparison loss algorithm to learn node representations for the graph generated post-random perturbation. Through parameter optimization using 5-fold cross-validation, our SGCLMD model achieves optimal area under the curve (AUC) and area under the precision-recall curve (AUPR) values of 0.8306 and 0.8751, respectively, representing improvements of 3 % and 3.1 % over the state-of-the-art method. Through ablation experiments and case studies, we validated the importance of graph enhancement methods and multi-view contrast learning modules, demonstrating SGCLMD's potential in predicting somatic mutation-drug associations. The code and dataset for SGCLMD are available at https://github.com/wangxiaosong96/SGCLMD.