The field of drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins, especially when such proteins directly influence disease progression. However, estimating binding affinity demands significant financial and computational resources. While state-of-the-art methodologies employ classical machine learning (ML) techniques, emerging hybrid quantum machine learning (QML) models have shown promise for enhanced performance, owing to their inherent parallelism and capacity to manage exponential increases in data dimensionality. Despite these advances, existing models encounter issues related to convergence stability and prediction accuracy. This paper introduces a novel hybrid quantum-classical deep learning model tailored for binding affinity prediction in drug discovery. Specifically, the proposed model synergistically integrates 3D and spatial graph convolutional neural networks within an optimized quantum architecture. Simulation results demonstrate a 6% improvement in prediction accuracy relative to existing classical models, as well as a significantly more stable convergence performance compared to previous classical approaches.

翻译：药物发现领域的关键在于准确预测潜在药物分子与靶蛋白之间的结合亲和力，尤其是当这些蛋白直接影响疾病进展时。然而，评估结合亲和力需要大量的财务和计算资源。尽管最先进的方法采用经典机器学习技术，但新兴的混合量子机器学习模型由于具有固有的并行性和处理数据维度指数级增长的能力，显示出提升性能的潜力。尽管取得了这些进展，现有模型在收敛稳定性和预测精度方面仍存在问题。本文提出了一种针对药物发现中结合亲和力预测的混合量子-经典深度学习模型。具体而言，该模型在优化的量子架构中协同整合了3D和空间图卷积神经网络。仿真结果表明，与现有经典模型相比，预测精度提高了6%，且收敛性能比以往的经典方法更为稳定。