A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery

TL;DR

This paper presents a hybrid quantum-classical neural network that improves drug binding affinity predictions, achieving higher accuracy and more stable convergence than classical models, aiding efficient drug discovery.

Contribution

It introduces a novel hybrid quantum-classical deep learning model combining 3D spatial graph CNNs with quantum architecture for enhanced binding affinity prediction.

Findings

6% improvement in prediction accuracy

More stable convergence performance

Effective integration of quantum and classical neural networks

Abstract

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.