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

arXiv:2309.03919v2 Announce Type: replace-cross
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 …

abstract art arxiv computational cs.lg discovery disease drug discovery financial fusion hybrid influence molecules network neural network prediction predictions protein proteins quant-ph quantum resources state type