Scoring functions for protein-ligand docking.
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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Scoring functions generally neglect entropic contributions or adopt simplified algorithms to approximate entropies.
“Given the difficulty in modeling entropic effects for ΔGbind, scoring functions generally neglect their contributions or adopt simplified algorithms to approximate entropies in a straightforward manner (Jain, 2006).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Jain, 2006
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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 · see citing works
In silico protein function prediction: the rise of machine learning-based approaches
Chen, J., Gu, Z., Lai, L., & Pei, J. (2023) · Medical Review · doi:10.1515/mr-2023-0038 · Cited by 21 · see citing works
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Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology
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- 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)
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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
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.
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