Synergistic Computational Approaches for Accelerated Drug Discovery: Integrating Quantum Mechanics, Statistical Thermodynamics, and Quantum Computing

TL;DR

This paper presents a hybrid quantum-classical computational framework that significantly accelerates drug discovery by accurately predicting protein-ligand binding energies with reduced computational cost, enabling high-throughput screening.

Contribution

It introduces a novel hybrid approach combining quantum mechanics, statistical thermodynamics, and quantum computing to improve accuracy and efficiency in binding free energy predictions.

Findings

Achieves ~1.10 kcal/mol mean absolute error in BFE predictions.

Reduces computational cost by approximately 20-fold compared to traditional methods.

Maintains strong rank-order correlation with established protocols.

Abstract

Accurately predicting protein-ligand binding free energies (BFEs) remains a central challenge in drug discovery, particularly because the most reliable methods, such as free energy perturbation (FEP), are computationally intensive and difficult to scale. Here, we introduce a hybrid quantum-classical framework that combines Mining Minima sampling with quantum mechanically refined ligand partial charges, QM/MM interaction evaluation, and variational quantum eigensolver (VQE)-based electronic energy correction. This design enables explicit treatment of polarization, charge redistribution, and electronic correlation effects that are often underestimated in purely classical scoring schemes, while retaining computational efficiency. Across 23 protein targets and 543 ligands, the method achieves a mean absolute error of about 1.10 kcal/mol with strong rank-order fidelity (Pearson R = 0.75, Spearman rho = 0.76, Kendall tau = 0.57), consistent with the performance of contemporary FEP protocols. Notably, the workflow requires only about 25 minutes per ligand on standard compute resources, resulting in an approximate 20-fold reduction in computational cost relative to alchemical free energy approaches. This level of accuracy and efficiency makes the method well-suited for high-throughput lead optimization and iterative design cycles in pharmaceutical discovery. The framework also provides a natural foundation for future integration with machine learning models to enable predictive, large-scale, and adaptive screening strategies.