CongFu: Conditional Graph Fusion for Drug Synergy Prediction

Drug synergy, characterized by the amplified combined effect of multiple drugs, presents a critical phenomenon for optimizing therapeutic outcomes. However, limited data on drug synergy, arising from the vast number of possible drug combinations and computational costs, motivate the need for predictive methods. In this work, we introduce CongFu, a novel Conditional Graph Fusion Layer, designed to predict drug synergy. CongFu employs an attention mechanism and a bottleneck to extract local graph contexts and conditionally fuse graph data within a global context. Its modular architecture enables flexible replacement of layer modules, including readouts and graph encoders, facilitating customization for diverse applications. To evaluate the performance of CongFu, we conduct comprehensive experiments on four datasets, encompassing three distinct setups for drug synergy prediction. Remarkably, CongFu achieves state-of-the-art results on 11 out of 12 benchmark datasets, demonstrating its ability to capture intricate patterns of drug synergy. Through extensive ablation studies, we validate the significance of individual layer components, affirming their contributions to overall predictive performance. By addressing the challenge of predicting drug synergy in untested drug pairs, CongFu opens new avenues for optimizing drug combinations and advancing personalized medicine.