Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE

Accurately measuring compound binding affinities is key to driving the pharmaceutical development process. Rigorous physics-based in silico approaches, particularly alchemical free energy methods, have become a gold-standard tool for estimating compound affinity changes. Here we present the results of a large-scale precompetitive collaborative assessment of relative binding free energy (RBFE) calculations generated by 15 pharmaceutical companies. We evaluate an open-source and MIT-licensed RBFE protocol from the Open Free Energy (OpenFE) ecosystem across both public and blinded private datasets, encompassing over 1,700 ligands in total. For the public dataset, the weighted RMSE across the 58 systems was 1.731.531.96 kcal/mol, with 10 of the systems reaching sub-kcal/mol accuracy. For the private dataset, the weighted RMSE across the 37 systems was 2.441.943.06 kcal/mol, with only 2 of the systems reaching sub-kcal/mol accuracy, reflecting the increased complexity of real-world drug discovery. The protocol’s performance was system-dependent, with no single dominant error source, indicating that accuracy is primarily influenced by input quality and transformation type. Overall, these benchmark results are encouraging and indicate that OpenFE is ready for large-scale industrial applications, with an “out-of-the-box” accuracy that approaches that of commercial solutions. While comparison against published FEP+ results, which were obtained after manual parameter optimization, shows comparable ranking statistics, a gap remains in error statistics, for which we outline possible paths toward improvement. The protocol meets key criteria required for production use in an industrial setting: it shows robust performance, generates reproducible results, and achieves both sufficient throughput and rapid convergence.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-05-21
DOI
https://doi.org/10.1021/acs.jcim.6c00089
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE

Vytautas Gapsys, Nupur Bansal, Hans E. Purkey, Irfan Alibay et al.
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
article

Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE

Vytautas Gapsys, Nupur Bansal, Hans E. Purkey, Irfan Alibay, Hendrik Göddeke, Sirish Kaushik Lakkaraju, Christina Schindler, David Dotson, Lev Tsidilkovski, Benedikt Frieg, William C. Swope, Joseph P. Bluck, James R. B. Eastwood, Richard Gowers, Alexander H. Williams, Wei BinQing, David W. Swenson, Benjamin Ries, Aniket Magarkar, Chaya Stern, Sergio Pérez-Conesa, Stephanie M. Linker, Alyssa Travitz, Hayk Saribekyan, Marco Klähn, Dominic Rufa, Lili Cao, Thomas Löhr, Hannah M. Baumann, Kira A. Campbell, Iván Pulido, Yao Wu, Michael M. Henry, David L. Mobley, Jenke Scheen, Howard Broughton, Ivy Zhang, John D. Chodera, Gary Tresadern, Thomas Steinbrecher, Patricia Suriana, Joshua T. Horton
article en

Abstract

Accurately measuring compound binding affinities is key to driving the pharmaceutical development process. Rigorous physics-based in silico approaches, particularly alchemical free energy methods, have become a gold-standard tool for estimating compound affinity changes. Here we present the results of a large-scale precompetitive collaborative assessment of relative binding free energy (RBFE) calculations generated by 15 pharmaceutical companies. We evaluate an open-source and MIT-licensed RBFE protocol from the Open Free Energy (OpenFE) ecosystem across both public and blinded private datasets, encompassing over 1,700 ligands in total. For the public dataset, the weighted RMSE across the 58 systems was 1.731.531.96 kcal/mol, with 10 of the systems reaching sub-kcal/mol accuracy. For the private dataset, the weighted RMSE across the 37 systems was 2.441.943.06 kcal/mol, with only 2 of the systems reaching sub-kcal/mol accuracy, reflecting the increased complexity of real-world drug discovery. The protocol’s performance was system-dependent, with no single dominant error source, indicating that accuracy is primarily influenced by input quality and transformation type. Overall, these benchmark results are encouraging and indicate that OpenFE is ready for large-scale industrial applications, with an “out-of-the-box” accuracy that approaches that of commercial solutions. While comparison against published FEP+ results, which were obtained after manual parameter optimization, shows comparable ranking statistics, a gap remains in error statistics, for which we outline possible paths toward improvement. The protocol meets key criteria required for production use in an industrial setting: it shows robust performance, generates reproducible results, and achieves both sufficient throughput and rapid convergence.

Journal of Chemical Information and Modeling
Roche (Switzerland) (CH), Biogen (United States) (US), Memorial Sloan Kettering Cancer Center (US), Bristol-Myers Squibb (Germany) (DE), Merck KGaA, Darmstadt (Germany) (DE), University of California, Irvine (US), Discovery Centre (CA), Fistula Foundation (US), Shoreline Community College (US), South College (US), Kettering University (US), United Biscuits (United Kingdom) (GB), GlaxoSmithKline (United States) (US), AstraZeneca (Brazil) (BR), Roche (China) (CN), AbbVie (Germany) (DE), Eli Lilly (Spain) (ES), Johnson & Johnson (Israel) (IL), Federal Research Center for Information and Computational Technologies (RU), Computational Physics (United States) (US), Free Energy (Slovakia) (SK), Boehringer Ingelheim (Australia) (AU), Molecular Discovery (United Kingdom) (GB), Open Molecular Software Foundation, Bayer (Germany) (DE)
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
DOISourceOpenAlex
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