A Hybrid Quantum-classical Fusion Neural Network to Improve Protein-ligand Binding Affinity Predictions for Drug Discovery

Drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins that influence disease progression, which is financially and computationally demanding. Although classical and hybrid quantum machine learning models have been employed in previous studies to aid in binding affinity prediction, they encounter several issues related to convergence stability and prediction accuracy. In this regard, 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 circuit 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. Moreover, to scalably deploy the proposed framework over today's noisy intermediate-scale quantum (NISQ) devices, a novel quantum error mitigation algorithm is proposed. This algorithm outperforms existing techniques and is capable of mitigating errors with gate noise probabilities, $p \leq 0.05$, while resulting in no additional overhead during the training and testing phases.