VQSynery: Robust Drug Synergy Prediction With Vector Quantization
  Mechanism
Jiawei Wu Mingyuan Yan Dianbo Liu
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.