Doing More with Less: Accurate and Scalable Ligand Free Energy Calculations by Focusing on the Binding Site

Predicting how chemical modifications affect drug binding is central to rational drug design. Free energy perturbation (FEP) calculations provide accurate estimates of these binding affinity changes, but existing methods often require substantial computational resources and expert knowledge. Here, we present QligFEP v2.1.0, a flexible open-source workflow based on a graphical and command-line interface for calculating relative binding free energies using spherical boundary conditions, which dramatically reduces simulation system size by confining simulations to a focused region around the binding site. QligFEP features a configurable restraint algorithm that automatically handles diverse chemical transformations, streamlined setup procedures, and enhanced analysis tools. We validated the method using industry benchmarks comprising 16 protein targets and 639 ligand transformations. Statistical analysis demonstrates that QligFEP achieves comparable accuracy to established commercial and open-source alternatives while requiring only a fraction of the computational resources. The perturbation protocol simulates ∼6250 atoms per perturbation leg and completes transformation replicates in under 2 h on standard computational clusters. Unlike full-system simulations, QligFEP's modest computational requirements make FEP accessible for less than $1 on current AWS spot instances. The combination of accuracy, flexibility, and computational efficiency positions QligFEP as a practical solution for accelerating compound optimization in drug discovery, making rigorous binding affinity predictions accessible for large scale applications and to research groups with limited computational infrastructure.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-02-13
DOI
https://doi.org/10.1021/acs.jcim.5c02932
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Doing More with Less: Accurate and Scalable Ligand Free Energy Calculations by Focusing on the Binding Site

Willem Jespers, Gerard J. P. van Westen, David Alencar Araripe, Johan Åqvist et al.
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
article

Doing More with Less: Accurate and Scalable Ligand Free Energy Calculations by Focusing on the Binding Site

Willem Jespers, Gerard J. P. van Westen, David Alencar Araripe, Johan Åqvist, Antti Poso, Hugo Gutiérrez-de-Terán, Alejandro Díaz-Holguín
article en

Abstract

Predicting how chemical modifications affect drug binding is central to rational drug design. Free energy perturbation (FEP) calculations provide accurate estimates of these binding affinity changes, but existing methods often require substantial computational resources and expert knowledge. Here, we present QligFEP v2.1.0, a flexible open-source workflow based on a graphical and command-line interface for calculating relative binding free energies using spherical boundary conditions, which dramatically reduces simulation system size by confining simulations to a focused region around the binding site. QligFEP features a configurable restraint algorithm that automatically handles diverse chemical transformations, streamlined setup procedures, and enhanced analysis tools. We validated the method using industry benchmarks comprising 16 protein targets and 639 ligand transformations. Statistical analysis demonstrates that QligFEP achieves comparable accuracy to established commercial and open-source alternatives while requiring only a fraction of the computational resources. The perturbation protocol simulates ∼6250 atoms per perturbation leg and completes transformation replicates in under 2 h on standard computational clusters. Unlike full-system simulations, QligFEP's modest computational requirements make FEP accessible for less than $1 on current AWS spot instances. The combination of accuracy, flexibility, and computational efficiency positions QligFEP as a practical solution for accelerating compound optimization in drug discovery, making rigorous binding affinity predictions accessible for large scale applications and to research groups with limited computational infrastructure.

Journal of Chemical Information and ModelingVol. 66(6)
Leiden University (NL), Uppsala University (SE), University of Groningen (NL), University of Eastern Finland (FI), Stichting MS Research (NL), Centre for Human Drug Research (NL), Nanomaterials and Nanotechnology Research Center (ES), Instituto de Investigación Sanitaria del Principado de Asturias (ES)
Industry, innovation and infrastructure
Openalex Percentile: Top 54%
Computational Drug Discovery Methods
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