Adaptive Lambda Scheduling: A Method for Computational Efficiency in Free Energy Perturbation Simulations

Recent increases in the availability of computational power have improved the accessibility of ligand-protein relative binding free energy (RBFE) calculations; however, these calculations remain resource-intensive, which can limit their practical application. RBFE calculations typically use a set of thermodynamic intermediates mediated by the transformation coordinate λ. Optimizing λ offers a way to tune the computational efforts required for a given RBFE calculation. Here, we present Adaptive Lambda Scheduling (ALS), a streamlined approach for on-the-fly bespoke λ scheduling. We show it can achieve substantial reductions in computational cost while retaining predictive performance.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-01-09
DOI
https://doi.org/10.1021/acs.jcim.4c01668
Citations
6
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
4.84
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article

Adaptive Lambda Scheduling: A Method for Computational Efficiency in Free Energy Perturbation Simulations

Scott D. Midgley, Mark Mackey, Matthew Habgood, Sofia Bariami
6 citations
Journal of Chemical Information and Modeling
Protein Structure and Dynamics
4.84
article

Adaptive Lambda Scheduling: A Method for Computational Efficiency in Free Energy Perturbation Simulations

Scott D. Midgley, Mark Mackey, Matthew Habgood, Sofia Bariami
article en
6 citations

Abstract

Recent increases in the availability of computational power have improved the accessibility of ligand-protein relative binding free energy (RBFE) calculations; however, these calculations remain resource-intensive, which can limit their practical application. RBFE calculations typically use a set of thermodynamic intermediates mediated by the transformation coordinate λ. Optimizing λ offers a way to tune the computational efforts required for a given RBFE calculation. Here, we present Adaptive Lambda Scheduling (ALS), a streamlined approach for on-the-fly bespoke λ scheduling. We show it can achieve substantial reductions in computational cost while retaining predictive performance.

Journal of Chemical Information and ModelingVol. 65(2)
Cresset (United Kingdom) (GB)
Affordable and clean energy
Openalex Percentile: Top 6%
Protein Structure and Dynamics
4.84
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