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

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.

References in corpus (6)

• Enhanced sampling methods for molecular dynamics simulations
• Force-Field-Enhanced Neural Network Interactions: from Local Equivariant Embedding to Atom-in-Molecule properties and long-range effects
• Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation
• Lambda-ABF: Simplified, Portable, Accurate and Cost-effective Alchemical Free Energy Computations
• FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials
• Pushing the Accuracy Limit of Foundation Neural Network Models with Quantum Monte Carlo Forces and Path Integrals