  Drug synergy, characterized by the amplified combined effect of multiple
drugs, is critically important for optimizing therapeutic outcomes. Limited
data on drug synergy, arising from the vast number of possible drug
combinations and testing 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. 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 ablation studies, we
validate the significance of individual layer components, affirming their
contributions to overall predictive performance. Finally, we propose an
explainability strategy for elucidating the effect of drugs on genes. By
addressing the challenge of predicting drug synergy in untested drug pairs and
utilizing our proposed explainability approach, CongFu opens new avenues for
optimizing drug combinations and advancing personalized medicine.
