Lambda-ABF-OPES: Faster Convergence with High Accuracy in Alchemical Free Energy Calculations

TL;DR

This paper introduces Lambda-ABF-OPES, a hybrid method combining Lambda-ABF and OPES, which significantly accelerates convergence and improves accuracy in alchemical free energy calculations for drug discovery.

Contribution

The paper presents a novel hybrid approach that enhances sampling efficiency and reduces computational cost in absolute binding free energy estimations.

Findings

Achieves up to nine-fold improvement in sampling efficiency.

Provides converged results at a fraction of standard computational costs.

Effective with the AMOEBA polarizable force field.

Abstract

Predicting the binding affinity between small molecules and target macromolecules while combining both speed and accuracy, is a cornerstone of modern computational drug discovery which is critical for accelerating therapeutic development. Despite recent progresses in molecular dynamics (MD) simulations, such as advanced polarizable force fields and enhanced-sampling techniques, estimating absolute binding free energies (ABFE) remains computationally challenging. To overcome these difficulties, we introduce a highly efficient, hybrid methodology that couples the Lambda-Adaptive Biasing Force (Lambda-ABF) scheme with On-the-fly Probability Enhanced Sampling (OPES). This approach achieves up to a nine-fold improvement in sampling efficiency and computational speed compared to the original Lambda-ABF when used in conjunction with the AMOEBA polarizable force field, yielding converged results at a fraction of the cost of standard techniques.