» Articles » PMID: 23705874

Evaluation and Optimization of Virtual Screening Workflows with DEKOIS 2.0--a Public Library of Challenging Docking Benchmark Sets

Overview
Date 2013 May 28
PMID 23705874
Citations 59
Authors
Affiliations
Soon will be listed here.
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.

Citing Articles

Evaluating the structure-based virtual screening performance of SARS-CoV-2 main protease: A benchmarking approach and a multistage screening example against the wild-type and Omicron variants.

Galal N, Beshay B, Soliman O, Ismail M, Abdelfadil M, El-Hadidi M PLoS One. 2025; 20(2):e0318712.

PMID: 39970175 PMC: 11838920. DOI: 10.1371/journal.pone.0318712.


SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.

Cao D, Chen M, Zhang R, Wang Z, Huang M, Yu J Nat Methods. 2024; 22(2):310-322.

PMID: 39604569 DOI: 10.1038/s41592-024-02516-y.


vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening.

Andrianov G, Haroldsen E, Karanicolas J Int J Mol Sci. 2024; 25(22).

PMID: 39596415 PMC: 11595162. DOI: 10.3390/ijms252212350.


Protein language models are performant in structure-free virtual screening.

Lam H, Guan J, Ong X, Pincket R, Mu Y Brief Bioinform. 2024; 25(6).

PMID: 39327890 PMC: 11427677. DOI: 10.1093/bib/bbae480.


Synthesis and elucidation of strained galactopyronose esters as selective cyclooxygenase-2 inhibitor: a comprehensive computational approach.

Musa M, Miah M, Munni Y, Patwary M, Kazi M, Matin M RSC Adv. 2024; 14(41):30469-30481.

PMID: 39318455 PMC: 11421415. DOI: 10.1039/d4ra03520h.