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Cited by 241Computational Drug Discovery MethodsSynthesis and Characterization of Heterocyclic Compoundsvaccines and immunoinformatics approaches

Consensus scoring criteria for improving enrichment in virtual screening.

Yang, Jinn-Moon, Chen, Yen-Fu, Shen, Tsai-Wei, Kristal, Bruce S., Hsu, D. Frank·2005·Journal of Chemical Information and Modeling
DOI 10.1021/ci050034w
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Abstract

MOTIVATION: Virtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. The major weakness of virtual screening-the inability to consistently identify true positives (leads)-is likely due to our incomplete understanding of the chemistry involved in ligand binding and the subsequently imprecise scoring algorithms. It has been demonstrated that combining multiple scoring functions (consensus scoring) improves the enrichment of true positives. Previous efforts at consensus scoring have largely focused on empirical results, but they have yet to provide a theoretical analysis that gives insight into real features of combinations and data fusion for virtual screening. RESULTS: We demonstrate that combining multiple scoring functions improves the enrichment of true positives only if (a) each of the individual scoring functions has relatively high performance and (b) the individual scoring functions are distinctive. Notably, these two prediction variables are previously established criteria for the performance of data fusion approaches using either rank or score combinations. This work, thus, establishes a potential theoretical basis for the probable success of data fusion approaches to improve yields in in silico screening experiments. Furthermore, it is similarly established that the second criterion (b) can, in at least some cases, be functionally defined as the area between the rank versus score plots generated by the two (or more) algorithms. Because rank-score plots are independent of the performance of the individual scoring function, this establishes a second theoretically defined approach to determining the likely success of combining data from different predictive algorithms. This approach is, thus, useful in practical settings in the virtual screening process when the performance of at least two individual scoring functions (such as in criterion a) can be estimated as having a high likelihood of having high performance, even if no training sets are available. We provide initial validation of this theoretical approach using data from five scoring systems with two evolutionary docking algorithms on four targets, thymidine kinase, human dihydrofolate reductase, and estrogen receptors of antagonists and agonists. Our procedure is computationally efficient, able to adapt to different situations, and scalable to a large number of compounds as well as to a greater number of combinations. Results of the experiment show a fairly significant improvement (vs single algorithms) in several measures of scoring quality, specifically "goodness-of-hit" scores, false positive rates, and "enrichment". This approach (available online at http://gemdock.life. nctu.edu.tw/dock/download.php) has practical utility for cases where the basic tools are known or believed to be generally applicable, but where specific training sets are absent.

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  • Combining different scoring functions in a consensus scoring scheme is a promising data fusion strategy to improve VS enrichment, pose, and affinity prediction.

    “The combination of different scoring functions on a scoring scheme (CS) is considered as a promising data fusion strategy to improve VS enrichment, pose, and affinity prediction (Charifson et al., 1999; Bissantz et al., 2000; Yang et al., 2005; Kaserer et al., 2015; Chaput et al., 2016a; Chaput and Mouawad, 2017; Ericksen et al., 2017).”

    From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Yang et al., 2005

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Authors
Yang, J.-M., Chen, Y.-F., Shen, T.-W., Kristal, B. S., & Hsu, D. F.
Affiliations
Fordham University (US)
ORCID
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2005-05-28
Volume
45
Issue
4
Pages
1134-1146
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/ci050034w
OpenAlex
W2080982083
Cited by
241
References
33
Citations by year
2026: 8 · 2025: 5 · 2024: 8 · 2023: 7 · 2022: 10 · 2021: 13 · 2020: 10 · 2019: 10 · 2018: 12 · 2017: 9 · 2016: 5 · 2015: 11 · 2014: 9 · 2013: 18 · 2012: 22
FWCI
11.42
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institute on Aging (R21AG024232, AG024232)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
virtual screening, consensus scoring, data fusion, ligand binding prediction, thymidine kinase, dihydrofolate reductase, antiestrogens, estrogen receptor agonists, false positive rate
MeSH terms
Algorithms, Enzyme Inhibitors, Folic Acid Antagonists, Ligands, Protein Binding, Receptors, Estrogen, Tetrahydrofolate Dehydrogenase, Thymidine Kinase, Drug Design, Databases, Factual

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