The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?
Open accessAbstract
Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein-ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein-ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data.
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Scoring Functions for Protein–Ligand Interactions
Sotriffer, C. (2012) · Protein‐Ligand Interactions · doi:10.1002/9783527645947.ch12 · Cited by 16
Cited by
Key Topics in Molecular Docking for Drug Design
Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva-Jr, F. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184574 · Cited by 641
Performance of machine-learning scoring functions in structure-based virtual screening.
Wójcikowski, M., Ballester, P. J., & Siedlecki, P. (2017) · Scientific Reports · doi:10.1038/srep46710 · Cited by 364
Self-citationAin, Q. U., Aleksandrova, A., Roessler, F. D., & Ballester, P. J. (2015) · WIREs Computational Molecular Science · doi:10.1002/wcms.1225 · Cited by 357
Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest
Wang, C., & Zhang, Y. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24667 · Cited by 334
Li, H., Leung, K., Wong, M., & Ballester, P. J. (2015) · Molecular Informatics · doi:10.1002/minf.201400132 · Cited by 252
Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations
Aldeghi, M., Heifetz, A., Bodkin, M. J., Knapp, S., & Biggin, P. C. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.6b11467 · Cited by 177
Beware of machine learning-based scoring functions-on the danger of developing black boxes.
Gabel, J., Desaphy, J., & Rognan, D. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500406k · Cited by 143
CSM-lig: a web server for assessing and comparing protein-small molecule affinities
Pires, D. E. V., & Ascher, D. B. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw390 · Cited by 115
Wang, Y., Guo, Y., Kuang, Q., Pu, X., Ji, Y., Zhang, Z., & Li, M. (2015) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-014-9827-y · Cited by 69
Harnessing systematic protein–ligand interaction fingerprints for drug discovery
Zhao, Z., & Bourne, P. E. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.07.004 · Cited by 41
Docking-Informed Machine Learning for Kinome-wide Affinity Prediction
Schifferstein, J., Bernatavicius, A., & Janssen, A. P. A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01260 · Cited by 8
Truong, C.-M., Ballester, P. J., Taboureau, O., & Tran-Nguyen, V.-K. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6c01143 · Cited by 2
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Metadata
- Authors
- Ballester, P. J., Schreyer, A., & Blundell, T. L.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-02-20
- Volume
- 54
- Issue
- 3
- Pages
- 944-955
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2075020622
- Cited by
- 194
- References
- 4
- FWCI
- 14.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
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