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.

本文介绍一种基于 CongFu 的全新条件图融合层，该层使用注意机制和瓶颈提取局部图形上下文，并在全局上下文中有条件地融合图形数据，用于预测药物协同作用，实验结果证明，CongFu 可以捕捉到药物协同作用的复杂模式，为优化药物组合和推进个性化医学开辟了新途径。