Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual Screening Article Swipe
Xu Min
,
Cheng Shen
,
Jincai Yang
,
Qing Wang
,
Niu Huang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1021/acsomega.2c05826
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1021/acsomega.2c05826
In recent years, large-scale structure-based virtual screening has attracted increasing levels of interest for identification of novel compounds corresponding to potential drug targets. It is critical to understand the strengths and weaknesses of docking algorithms to increase the success rate in practical applications. Here, we systematically investigated the docking successes and failures of two representative docking programs: UCSF DOCK 3.7 and AutoDock Vina. DOCK 3.7 performed better in early enrichment on the Directory of Useful Decoys: Enhanced (DUD-E) data set, although both docking methods were roughly comparable in overall enrichment performance. DOCK 3.7 also showed superior computational efficiency. Intriguingly, the Vina scoring function showed a bias toward compounds with higher molecular weights. Both the tested docking approaches yielded incorrectly predicted ligand binding poses caused by the limitations of torsion sampling. Based on a careful analysis of docking results from six representative cases, we propose the reasons underlying docking failures; furthermore, we provide a few solutions, representing practical guidance for large-scale virtual screening campaigns and future docking algorithm development.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acsomega.2c05826
- OA Status
- gold
- Cited By
- 24
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306658293
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4306658293Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1021/acsomega.2c05826Digital Object Identifier
- Title
-
Systematic Investigation of Docking Failures in Large-Scale Structure-Based Virtual ScreeningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-10-17Full publication date if available
- Authors
-
Xu Min, Cheng Shen, Jincai Yang, Qing Wang, Niu HuangList of authors in order
- Landing page
-
https://doi.org/10.1021/acsomega.2c05826Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1021/acsomega.2c05826Direct OA link when available
- Concepts
-
DOCK, Docking (animal), Virtual screening, Computer science, Protein–ligand docking, AutoDock, Computational biology, Drug discovery, Data mining, Artificial intelligence, Bioinformatics, Chemistry, Biology, In silico, Biochemistry, Nursing, Medicine, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 9, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
77Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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