ArticleJournal of chemical information and modeling2015
Comparative modeling and benchmarking data sets for human histone deacetylases and sirtuin families.
Article in Journal of chemical information and modeling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 27 citations in OpenAlex.
- A unique ligand-steered strategy for CC chemokine receptor 2 homology modeling to facilitate structure-based virtual screening.Chemical biology & drug design · 2021Article
- Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement.International journal of molecular sciences · 2020Review
- Review
- A prospective compound screening contest identified broader inhibitors for Sirtuin 1.Scientific reports · 2019Article
- Key Topics in Molecular Docking for Drug Design.International journal of molecular sciences · 2019Review
- MoleGear: A Java-Based Platform for Evolutionary De Novo Molecular Design.Molecules (Basel, Switzerland) · 2019Article
- The discovery of novel HDAC3 inhibitors via virtual screening and in vitro bioassay.Journal of enzyme inhibition and medicinal chemistry · 2018Article
- Screening of cytochrome P450 3A4 inhibitorsRSC advances · 2018Article
- Decoys Selection in Benchmarking Datasets: Overview and Perspectives.Frontiers in pharmacology · 2018Review
- A Thoroughly Validated Virtual Screening Strategy for Discovery of Novel HDAC3 Inhibitors.International journal of molecular sciences · 2017Article
- Virtual screening and experimental validation of novel histone deacetylase inhibitors.BMC pharmacology & toxicology · 2016Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
Histone deacetylases (HDACs) are an important class of drug targets for the treatment of cancers, neurodegenerative diseases, and other types of diseases. Virtual screening (VS) has become fairly effective approaches for drug discovery of novel and highly selective histone deacetylase inhibitors (HDACIs). To facilitate the process, we constructed maximal unbiased benchmarking data sets for HDACs (MUBD-HDACs) using our recently published methods that were originally developed for building unbiased benchmarking sets for ligand-based virtual screening (LBVS). The MUBD-HDACs cover all four classes including Class III (Sirtuins family) and 14 HDAC isoforms, composed of 631 inhibitors and 24609 unbiased decoys. Its ligand sets have been validated extensively as chemically diverse, while the decoy sets were shown to be property-matching with ligands and maximal unbiased in terms of "artificial enrichment" and "analogue bias". We also conducted comparative studies with DUD-E and DEKOIS 2.0 sets against HDAC2 and HDAC8 targets and demonstrate that our MUBD-HDACs are unique in that they can be applied unbiasedly to both LBVS and SBVS approaches. In addition, we defined a novel metric, i.e. NLBScore, to detect the "2D bias" and "LBVS favorable" effect within the benchmarking sets. In summary, MUBD-HDACs are the only comprehensive and maximal-unbiased benchmark data sets for HDACs (including Sirtuins) that are available so far. MUBD-HDACs are freely available at http://www.xswlab.org/ .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.