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。