A causal inference framework for identifying essential genes to enhance drug synergy prediction.

Abstract

Identifying synergistic drug combinations holds promise for more effective treatment strategies. Recent deep learning methods such as Transformers and Graph Neural Networks have shown improved predictive performance, but most of them integrate drug and cell line representations without explicitly modelling the causal effects of genes in mediating drug responses. We introduce CADS (Causal Adjustment for Drug Synergy), a deep learning framework that explicitly models the gene-drug causal relationships to improve both prediction accuracy and biological interpretability. CADS integrates multi-omics data with a learnable gene-selection mechanism that performs causal backdoor adjustment, enabling both drug synergy prediction and causal gene discovery. Across multiple benchmark datasets, CADS consistently achieves superior performance compared with state-of-the-art drug synergy prediction models. In addition, downstream analyses on case studies demonstrate that the inferred gene causal scores can recover clinically validated cancer-related genes involved in drug combinations. These results demonstrate that explicitly modelling causal genetic effects can enhance the reliability and interpretability of drug synergy prediction. The source code of CADS can be found at https://github.com/HuaiwuZhang/causalDC.