Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field
Abstract
Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.
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More rigorous free energy methods such as alchemical binding free energy calculations have become established for the computation of binding free energies of small molecules to proteins with MD.
“More rigorous free energy methods such as alchemical binding free energy calculations have become established for the computation of binding free energies of small molecules to proteins with MD.55−60”
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- Authors
- Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., Lupyan, D., Robinson, S., Dahlgren, M. K., Greenwood, J., Romero, D. L., Masse, C., Knight, J. L., Steinbrecher, T., Beuming, T., Damm, W., Harder, E., Sherman, W., Brewer, M., Wester, R., Murcko, M., Frye, L., Farid, R., Lin, T., Mobley, D. L., Jorgensen, W. L., Berne, B. J., Friesner, R. A., & Abel, R.
- Affiliations
- Schrodinger (United States) (US)
- Nimbus Discovery, 25 First Street, Suite 404, Cambridge, Massachusetts 02141, United States
- University of California, Irvine (US)
- Yale University (US)
- Columbia University (US)
- Corresponding author
- Abel, Robert
- ORCID
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-02-12
- Volume
- 137
- Issue
- 7
- Pages
- 2695-2703
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2154670681
- Cited by
- 1,313
- References
- 0
- Citations by year
- 2026: 135 · 2025: 128 · 2024: 137 · 2023: 138 · 2022: 137 · 2021: 123 · 2020: 129 · 2019: 83 · 2018: 108 · 2017: 80 · 2016: 87 · 2015: 26 · 2012: 1
- FWCI
- 104.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Chemical Society (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › thermodynamics and calorimetric analyses
- Keywords
- free energy calculation, force fields, lead optimization, binding affinity prediction, retrospective validation, false positive reduction, protein-ligand binding
- MeSH terms
- Ligands, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Thermodynamics, Drug Design, Computational Biology, Drug Discovery
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