  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.
