GFscore: a general nonlinear consensus scoring function for high-throughput docking.
Abstract
Most of the recent published works in the field of docking and scoring protein/ligand complexes have focused on ranking true positives resulting from a Virtual Library Screening (VLS) through the use of a specified or consensus linear scoring function. In this work, we present a methodology to speed up the High Throughput Screening (HTS) process, by allowing focused screens or for hitlist triaging when a prohibitively large number of hits is identified in the primary screen, where we have extended the principle of consensus scoring in a nonlinear neural network manner. This led us to introduce a nonlinear Generalist scoring Function, GFscore, which was trained to discriminate true positives from false positives in a data set of diverse chemical compounds. This original Generalist scoring Function is a combination of the five scoring functions found in the CScore package from Tripos Inc. GFscore eliminates up to 75% of molecules, with a confidence rate of 90%. The final result is a Hit Enrichment in the list of molecules to investigate during a research campaign for biological active compounds where the remaining 25% of molecules would be sent to in vitro screening experiments. GFscore is therefore a powerful tool for the biologist, saving both time and money.
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Nonlinear consensus scoring models were developed to improve pose prediction and ranking of compounds in VS experiments.
“Nonlinear CS models were also developed to improve pose prediction and ranking compounds in VS experiments (Betzi et al., 2006; Teramoto and Fukunishi, 2007; Ashtawy and Mahapatra, 2015; Ericksen et al., 2017).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Betzi et al., 2006
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- Authors
- Betzi, S., Suhre, K., Chétrit, B., Guerlesquin, F., & Morelli, X.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Morelli, Xavier
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2006-06-21
- Volume
- 46
- Issue
- 4
- Pages
- 1704-1712
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2074218294
- Cited by
- 51
- References
- 26
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 1 · 2022: 6 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 2 · 2017: 1 · 2016: 5 · 2015: 3 · 2014: 3 · 2013: 1 · 2012: 4
- FWCI
- 3.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Nella and Leon Benoziyo Center for Neurological Diseases, Weizmann Institute of Science
- Genopole
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- consensus scoring, protein-ligand docking, high-throughput screening
- MeSH terms
- Molecular Structure, Quantitative Structure-Activity Relationship
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