Evaluation and optimization of virtual screening workflows with DEKOIS 2.0 – A public library of challenging docking benchmark sets.
Abstract
The application of molecular benchmarking sets helps to assess the actual performance of virtual screening (VS) workflows. To improve the efficiency of structure-based VS approaches, the selection and optimization of various parameters can be guided by benchmarking. With the DEKOIS 2.0 library, we aim to further extend and complement the collection of publicly available decoy sets. Based on BindingDB bioactivity data, we provide 81 new and structurally diverse benchmark sets for a wide variety of different target classes. To ensure a meaningful selection of ligands, we address several issues that can be found in bioactivity data. We have improved our previously introduced DEKOIS methodology with enhanced physicochemical matching, now including the consideration of molecular charges, as well as a more sophisticated elimination of latent actives in the decoy set (LADS). We evaluate the docking performance of Glide, GOLD, and AutoDock Vina with our data sets and highlight existing challenges for VS tools. All DEKOIS 2.0 benchmark sets will be made accessible at http://www.dekois.com.
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DEKOIS 2.0 is composed of 81 benchmarking sets for 80 protein targets of therapeutic relevance, including nonconventional targets such as protein-protein interaction complexes.
“DEKOIS 2.0 is composed of 81 benchmarking sets for 80 protein targets of therapeutic relevance, including nonconventional targets such as protein–protein interaction complexes (Bauer et al., 2013).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Bauer et al., 2013
PDBbind, DUD-E, and DEKOIS 2.0 are examples of widely used benchmarks for evaluating scoring functions.
“PDBbind (Liu et al., 2015), DUD-E (Mysinger et al., 2012), and DEKOIS 2.0 (Bauer et al., 2013) are examples of widely used benchmarks for evaluating scoring functions.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Bauer et al., 2013
Little attention has been paid to careful preparation of protein-ligand structures, usually relying on automatic procedures.
“Despite many efforts made for collecting even more extensive and better quality datasets, little attention has been paid to the careful preparation of the protein–ligand structures, usually relying on automatic procedures (Bauer et al., 2013).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Bauer et al., 2013
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- Authors
- Bauer, M. R., Ibrahim, T. M., Vogel, S. M., & Boeckler, F. M.
- Affiliations
- University of Tübingen (DE)
- Corresponding author
- Boeckler, Frank M.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-06-12
- Volume
- 53
- Issue
- 6
- Pages
- 1447-1462
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2079331631
- Cited by
- 200
- References
- 89
- Citations by year
- 2026: 26 · 2025: 26 · 2024: 18 · 2023: 26 · 2022: 18 · 2021: 16 · 2020: 15 · 2019: 16 · 2018: 7 · 2017: 8 · 2016: 7 · 2015: 9 · 2014: 7 · 2013: 1
- FWCI
- 8.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deutscher Akademischer Austauschdienst
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Health Sciences › Medicine › Pharmacology › Cholinesterase and Neurodegenerative Diseases
- Keywords
- virtual screening, structure-based virtual screening, ligand selection, molecular charge, BindingDB, glides, gold, AutoDock Vina
- MeSH terms
- Humans, Protein Binding, Proteins, Drug Design, Databases, Protein, Workflow, Molecular Docking Simulation
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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