Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling

Michael T. Robo, Ryan L. Hayes, Xinqiang Ding, Brian Pulawski and Jonah Z. Vilseck ()
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Michael T. Robo: Indiana University School of Medicine
Ryan L. Hayes: University of California, Irvine
Xinqiang Ding: Massachusetts Institute of Technology
Brian Pulawski: Indiana University School of Medicine
Jonah Z. Vilseck: Indiana University School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Relative binding free energy calculations have become an integral computational tool for lead optimization in structure-based drug design. Classical alchemical methods, including free energy perturbation or thermodynamic integration, compute relative free energy differences by transforming one molecule into another. However, these methods have high operational costs due to the need to perform many pairwise perturbations independently. To reduce costs and accelerate molecular design workflows, we present a method called λ-dynamics with bias-updated Gibbs sampling. This method uses dynamic biases to continuously sample between multiple ligand analogues collectively within a single simulation. We show that many relative binding free energies can be determined quickly with this approach without compromising accuracy. For five benchmark systems, agreement to experiment is high, with root mean square errors near or below 1.0 kcal mol−1. Free energy results are consistent with other computational approaches and within statistical noise of both methods (0.4 kcal mol−1 or less). Notably, large efficiency gains over thermodynamic integration of 18–66-fold for small perturbations and 100–200-fold for whole aromatic ring substitutions are observed. The rapid determination of relative binding free energies will enable larger chemical spaces to be more readily explored and structure-based drug design to be accelerated.

Date: 2023
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DOI: 10.1038/s41467-023-44208-9

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