The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Validation and Use of the MM-PBSA Approach for Drug Discovery
Abstract
The MM-PBSA approach has become a popular method for calculating binding affinities of biomolecular complexes. Published application examples focus on small test sets and few proteins and, hence, are of limited relevance in assessing the general validity of this method. To further characterize MM-PBSA, we report on a more extensive study involving a large number of ligands and eight different proteins. Our results show that applying the MM-PBSA energy function to a single, relaxed complex structure is an adequate and sometimes more accurate approach than the standard free energy averaging over molecular dynamics snapshots. The use of MM-PBSA on a single structure is shown to be valuable (a) as a postdocking filter in further enriching virtual screening results, (b) as a helpful tool to prioritize de novo design solutions, and (c) for distinguishing between good and weak binders (DeltapIC(50) > or = 2-3), but rarely to reproduce smaller free energy differences.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 41,298
Particle mesh Ewald: An N ⋅log( N ) method for Ewald sums in large systems
Darden, T., York, D., & Pedersen, L. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464397 · Cited by 30,546
Ryckaert, J.-P., Ciccotti, G., & Berendsen, H. J. C. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90098-5 · Cited by 21,239
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 20,598
Development and testing of a general amber force field
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A., & Case, D. A. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20035 · Cited by 19,506
A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules
Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W., & Kollman, P. A. (1995) · Journal of the American Chemical Society · doi:10.1021/ja00124a002 · Cited by 13,103
Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W., et al. (2000) · Accounts of Chemical Research · doi:10.1021/ar000033j · Cited by 4,978
Self-citationJakalian, A., Jack, D. B., & Bayly, C. I. (2002) · Journal of Computational Chemistry · doi:10.1002/jcc.10128 · Cited by 3,426
Free energy calculations: Applications to chemical and biochemical phenomena
Kollman, P. (1993) · Chemical Reviews · doi:10.1021/cr00023a004 · Cited by 2,101
Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V., & Mee, R. P. (1997) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1007996124545 · Cited by 1,686
Cited by
Hou, T., Wang, J., Li, Y., & Wang, W. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci100275a · Cited by 2,773
Lyne, P. D., Lamb, M. L., & Saeh, J. C. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm060522a · Cited by 699
Huang, S.-Y., Grinter, S. Z., & Zou, X. (2010) · Physical Chemistry Chemical Physics · doi:10.1039/c0cp00151a · Cited by 508
Predicting the Impact of Missense Mutations on Protein–Protein Binding Affinity
Li, M., Petukh, M., Alexov, E., & Panchenko, A. R. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct401022c · Cited by 111
In Silico Modeling and Scoring of PROTAC-Mediated Ternary Complex Poses
Liao, J., Nie, X., Unarta, I. C., Ericksen, S. S., & Tang, W. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c02155 · Cited by 62
Metadata
- Authors
- Kuhn, B., Gerber, P., Schulz-Gasch, T., & Stahl, M.
- Venue
- Journal of Medicinal Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2005-05-10
- Volume
- 48
- Issue
- 12
- Pages
- 4040-4048
- ISSN
- 0022-2623 (ISSN-L)
- 1520-4804
- OpenAlex
- W2048214699
- Cited by
- 462
- References
- 37
- FWCI
- 18.20
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Validation and Use of the MM-PBSA Approach for Drug Discovery. https://app.cochat.ai/papers/10.1021/jm049081q
- The paper
- Kuhn, B., Gerber, P., Schulz-Gasch, T., & Stahl, M. (2005). Validation and Use of the MM-PBSA Approach for Drug Discovery. Journal of Medicinal Chemistry. https://doi.org/10.1021/jm049081q
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
