Protein-based virtual screening of chemical databases. 1, evaluation of different docking/scoring combinations.

Bissantz, Caterina; Folkers, Gerd; Rognan, Didier

doi:10.1021/jm001044l

Cited by 740Computational Drug Discovery MethodsCholinesterase and Neurodegenerative DiseasesProtein Interaction Studies and Fluorescence Analysis

Protein-based virtual screening of chemical databases. 1, evaluation of different docking/scoring combinations.

Bissantz, Caterina, Folkers, Gerd, Rognan, Didier·2000·Journal of Medicinal Chemistry

DOI 10.1021/jm001044l

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Abstract

Three different database docking programs (Dock, FlexX, Gold) have been used in combination with seven scoring functions (Chemscore, Dock, FlexX, Fresno, Gold, Pmf, Score) to assess the accuracy of virtual screening methods against two protein targets (thymidine kinase, estrogen receptor) of known three-dimensional structure. For both targets, it was generally possible to discriminate about 7 out of 10 true hits from a random database of 990 ligands. The use of consensus lists common to two or three scoring functions clearly enhances hit rates among the top 5% scorers from 10% (single scoring) to 25-40% (double scoring) and up to 65-70% (triple scoring). However, in all tested cases, no clear relationships could be found between docking and ranking accuracies. Moreover, predicting the absolute binding free energy of true hits was not possible whatever docking accuracy was achieved and scoring function used. As the best docking/consensus scoring combination varies with the selected target and the physicochemistry of target-ligand interactions, we propose a two-step protocol for screening large databases: (i) screening of a reduced dataset containing a few known ligands for deriving the optimal docking/consensus scoring scheme, (ii) applying the latter parameters to the screening of the entire database.

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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 Bissantz et al., 2000

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Authors: Bissantz, C., Folkers, G., & Rognan, D.
Affiliations: ETH Zurich (CH)
ORCID: Folkers, Gerd
Rognan, Didier
Venue: Journal of Medicinal Chemistry
Publisher: American Chemical Society (ACS)
Published: 2000-11-22
Volume: 43
Issue: 25
Pages: 4759-4767
ISSN: 0022-2623 (ISSN-L)
1520-4804
DOI: 10.1021/jm001044l
OpenAlex: W2040496005
Cited by: 740
References: 52
Citations by year: 2026: 11 · 2025: 13 · 2024: 18 · 2023: 18 · 2022: 29 · 2021: 31 · 2020: 22 · 2019: 13 · 2018: 15 · 2017: 16 · 2016: 19 · 2015: 26 · 2014: 21 · 2013: 30 · 2012: 28
FWCI: 40.80
Type: journal article
Language: en
Open access: closed
Locations: Journal of Medicinal Chemistry (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Health Sciences › Medicine › Pharmacology › Cholinesterase and Neurodegenerative Diseases
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
Keywords: virtual screening, protein-ligand docking, scoring functions, consensus scoring, docking accuracy, hit rate, thymidine kinase, estrogen receptor, binding free energy prediction
MeSH terms: Algorithms, Estrogen Antagonists, Ligands, Models, Molecular, Receptors, Estrogen, Thymidine Kinase, Databases, Factual, Estrogen Receptor alpha

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Protein-based virtual screening of chemical databases. 1, evaluation of different docking/scoring combinations.

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