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Using AMBER18 for Relative Free Energy Calculations
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Abstract
With renewed interest in free energy methods in contemporary structure-based drug design, there is a pressing need to validate against multiple targets and force fields to assess the overall ability of these methods to accurately predict relative binding free energies. We computed relative binding free energies using graphics processing unit accelerated thermodynamic integration (GPU-TI) on a data set originally assembled by Schrödinger, Inc. Using their GPU free energy code (FEP+) and the OPLS2.1 force field combined with the REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol. In our study using GPU-TI from AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 perturbations contained in this data set. A more detailed analysis of our results suggested that the observed differences between the two studies arise from differences in sampling protocols along with differences in the force fields employed. Future work should address the problem of establishing benchmark quality results with robust statistical error bars obtained through multiple independent runs and enhanced sampling, which is possible with the GPU-accelerated features in AMBER.
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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.55−60”
From Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints (2025) · cites this paper as [59]
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- Case D. A., 2016, AMBER 2016
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Metadata
- Authors
- Song, L. F., Lee, T.-S., Zhu, C., York, D. M., & Merz, K. M.
- Affiliations
- Michigan State University (US)
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Merz, Kenneth M.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-06-06
- Volume
- 59
- Issue
- 7
- Pages
- 3128-3135
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC7371000
- OpenAlex
- W2949467224
- Cited by
- 234
- References
- 62
- Citations by year
- 2026: 20 · 2025: 32 · 2024: 37 · 2023: 35 · 2022: 45 · 2021: 30 · 2020: 31 · 2019: 4
- FWCI
- 18.88
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Royal College of Surgeons in Ireland (submittedVersion, cc-by)
- Funders
- Efficacy and Mechanism Evaluation Programme (GM107485)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- relative binding free energy, thermodynamic integration, GPU acceleration, structure-based drug design, benchmarking, enhanced sampling, REST2, free energy perturbation, ligand binding prediction
- MeSH terms
- Computer Graphics, Models, Molecular, Protein Binding, Software, Thermodynamics, Drug Design, Molecular Structure, Molecular Dynamics Simulation
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