The pursuit of optimizing cancer therapies is significantly advanced by the
accurate prediction of drug synergy. Traditional methods, such as clinical
trials, are reliable yet encumbered by extensive time and financial demands.
The emergence of high-throughput screening and computational innovations has
heralded a shift towards more efficient methodologies for exploring drug
interactions. In this study, we present VQSynergy, a novel framework that
employs the Vector Quantization (VQ) mechanism, integrated with gated residuals
and a tailored attention mechanism, to enhance the precision and
generalizability of drug synergy predictions. Our findings demonstrate that
VQSynergy surpasses existing models in terms of robustness, particularly under
Gaussian noise conditions, highlighting its superior performance and utility in
the complex and often noisy domain of drug synergy research. This study
underscores the potential of VQSynergy in revolutionizing the field through its
advanced predictive capabilities, thereby contributing to the optimization of
cancer treatment strategies.

本研究提出了 VQSynergy 框架，利用向量量化（VQ）机制、门控残差和定制的注意机制来增强药物协同效应预测的精确性和泛化能力，结果表明 VQSynergy 在鲁棒性方面超过现有的模型，尤其是在高斯噪声条件下，强调其在药物协同研究的复杂和常常存在噪声的领域中具有卓越的性能和实用性，以此突出了 VQSynergy 在通过先进的预测能力为癌症治疗策略优化方面的潜力。

VQSynery：基于向量量化机制的稳健药物协同作用预测

VQSynery: Robust Drug Synergy Prediction With Vector Quantization  Mechanism

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。