Cited by 259Computational Drug Discovery MethodsProtein Structure and Dynamicsvaccines and immunoinformatics approaches

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Scoring functions for protein-ligand docking.

Bentham Science Publisher, Bentham Science Publisher·2006·Current Protein & Peptide Science

DOI 10.2174/138920306778559395

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Abstract

Virtual screening by molecular docking has become established as a method for drug lead discovery and optimization. All docking algorithms make use of a scoring function in combination with a method of search. Two theoretical aspects of scoring function performance dominate operational performance. The first is the degree to which a scoring function has a global extremum within the ligand pose landscape at the proper location. The second is the degree to which the magnitude of the function at the extremum is accurate. Presuming adequate search strategies, a scoring function's location performance will dominate behavior with respect to docking accuracy: the degree to which a predicted pose of a ligand matches experimental observation. A scoring function's magnitude performance will dominate behavior with respect to screening utility: enrichment of true ligands over non-ligands. Magnitude estimation also controls pure scoring accuracy: the degree to which bona fide ligands of a particular protein may be correctly ranked. Approaches to the development of scoring functions have varied widely, with a number of functions yielding similarly high levels of performance relating to the location issue. However, even among functions performing equally well on location, widely varying performance is observed on the question of magnitude. In many cases, performance is good enough to yield high enrichments of true ligands versus non-ligands in screening across a wide variety of protein types. Generally, performance is not good enough to correctly rank among true ligands. Strategies for improvement are discussed.

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“According to how they are derived, current scoring functions can be grouped into three categories: force field scoring functions, empirical scoring function, and mean-force scoring functions [7–11].”
Background
Genetic algorithm with a crossover elitist preservation mechanism for protein–ligand docking
Guan, B., Zhang, C., & Ning, J. (2017) · AMB Express · doi:10.1186/s13568-017-0476-0 · Cited by 26
“Scoring function (Hu et al. 2004; Huey et al. 2006; Jain 2006; Muryshev et al. 2003) and search algorithm (Blum et al. 2011; L ό pez-Camacho et al. 2015) are two important parts in the process of protein–ligand docking.”
Background
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Pencheva, T., Soumana, O. S., Pajeva, I. K., & Miteva, M. A. (2010) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2009.12.025 · Cited by 24
Tracing the Detail: How Mutations Affect Binding Modes and Thermodynamic Signatures of Closely Related Aldose Reductase Inhibitors
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In Silico ADME Techniques Used in Early-Phase Drug Discovery
Danielson, M. L., Hu, B., Shen, J., & Desai, P. V. (2017) · AAPS advances in the pharmaceutical sciences series · doi:10.1007/978-3-319-50042-3_4 · Cited by 25
In silico protein function prediction: the rise of machine learning-based approaches
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Discovery of new JNK3 inhibitory chemotypes via QSAR-Guided selection of docking-based pharmacophores and comparison with other structure-based pharmacophore modeling methods
Tuffaha, G. O., Hatmal, M. M., & Taha, M. O. (2019) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2019.05.015 · Cited by 24
Assessing the Structural and Pharmacological Similarity of Newly Identified Drugs of Abuse to Controlled Substances Using Public Health Assessment via Structural Evaluation
Ellis, C. R., Racz, R., Kruhlak, N. L., Kim, M. T., Hawkins, E. G., Strauss, D. G., & Stavitskaya, L. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1418 · Cited by 23
Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology
Da, C., Zhang, D., Stashko, M. A., Vasileiadi, E., Parker, R. E., Minson, K. A., Huey, M. G., Huelse, J. M., Hunter, D. M., Gilbert, T. S. K., et al. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.9b08660 · Cited by 21

Authors: Bentham Science Publisher, B. S. P.
Venue: Current Protein & Peptide Science
Publisher: Bentham Science Publishers Ltd.
Published: 2006-10-01
Volume: 7
Issue: 5
Pages: 407-420
ISSN: 1389-2037 (ISSN-L)
DOI: 10.2174/138920306778559395
OpenAlex: W1512801428
Cited by: 258
References: 0
Citations by year: 2026: 4 · 2025: 8 · 2024: 11 · 2023: 21 · 2022: 32 · 2021: 16 · 2020: 15 · 2019: 16 · 2018: 12 · 2017: 12 · 2016: 14 · 2015: 8 · 2014: 6 · 2013: 6 · 2012: 13
FWCI: 12.94
Type: journal article
Language: en
Open access: closed
Locations: Current Protein and Peptide Science (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health
National Institute of General Medical Sciences (GM070781, R01GM070481)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords: protein-ligand docking, scoring functions, virtual screening, docking accuracy, enrichment, ligand ranking, lead compound identification
MeSH terms: Animals, Drug Evaluation, Preclinical, Ligands, Models, Chemical, Protein Binding, Proteins, Research Design

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