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.
@article{arxiv.2502.17233,
title = {Lambda-ABF-OPES: Faster Convergence with High Accuracy in Alchemical Free Energy Calculations},
author = {Narjes Ansari and Zhifeng Francis Jing and Antoine Gagelin and Florent Hédin and Félix Aviat and Jérôme Hénin and Jean-Philip Piquemal and Louis Lagardère},
journal= {arXiv preprint arXiv:2502.17233},
year = {2025}
}