TranSynergy: Mechanism-Driven Interpretable Deep Neural Network for the Synergistic Prediction and Pathway Deconvolution of Drug Combinations

Abstract

AbstractMotivationDrug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. With the fast-growing number of anti-cancer drugs, the experimental investigation of all drug combinations is costly and time-consuming. Computational techniques can improve the efficiency of drug combination screening. Despite recent advances in applying machine learning to synergistic drug combinations prediction, several challenges remain. First, the performance of existing methods is suboptimal. There is still much space for improvement. Second, biological knowledge has not been fully incorporated into the model. Finally, many models are lack of interpretability, limiting their clinical applications.ResultsWe develop a knowledge-enabled and self-attention boosted deep learning model, TranSynergy, to improve the performance and interpretability of synergistic drug combinations prediction. TranSynergy is well designed such that cellular effect of drug actions can be explicitly modeled through cell-line gene dependency, gene-gene interaction, and genome-wide drug-target interaction. A novel Shapley Additive Gene Set Enrichment Analysis (SA-GSEA) method is developed to deconvolute biological pathways that contribute to the synergistic drug combination and improve model interpretability. Extensive benchmark studies demonstrate that TranSynergy significantly outperforms the state-of-the-art method, suggesting the potential of mechanism-driven machine learning. Novel pathways that are associated with the synergistic combinations are revealed and supported by experimental evidence. They may provide new insights into identifying biomarkers for precision medicine and discovering new anti-cancer therapies. Several new synergistic drug combinations are predicted with high confidence for ovarian cancer which has few treatment options.AvailabilityThe code is available at https://github.com/qiaoliuhub/drug_combinationContactlxie@iscb.org