ArticleJournal of chemical information and modeling2014
An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs.
Article in Journal of chemical information and modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- Building shape-focused pharmacophore models for effective docking screening.Journal of cheminformatics · 2024Article
- Multimodal data fusion for supervised learning-based identification of USP7 inhibitors: a systematic comparison.Journal of cheminformatics · 2023Article
- Combination of Docking-Based and Pharmacophore-Based Virtual Screening Identifies Novel Agonists That Target the Urotensin Receptor.Molecules (Basel, Switzerland) · 2022Article
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning.International journal of molecular sciences · 2021Review
- Virtual screening and drug repurposing experiments to identify potential novel selective MAO-B inhibitors for Parkinson's disease treatment.Molecular diversity · 2021Article
- FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening.Journal of chemical information and modeling · 2021Article
- A unique ligand-steered strategy for CC chemokine receptor 2 homology modeling to facilitate structure-based virtual screening.Chemical biology & drug design · 2021Article
- Property-Unmatched Decoys in Docking Benchmarks.Journal of chemical information and modeling · 2021Article
- Article
- The discovery of novel HDAC3 inhibitors via virtual screening and in vitro bioassay.Journal of enzyme inhibition and medicinal chemistry · 2018Article
- FINDSITEJournal of chemical information and modeling · 2018Article
- Deep learning and virtual drug screening.Future medicinal chemistry · 2018Article
- Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis.Journal of chemical information and modeling · 2018Article
- Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.Current pharmaceutical design · 2018Review
- 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
- Getting the most out of PubChem for virtual screening.Expert opinion on drug discovery · 2016Review
- Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors.International journal of molecular sciences · 2015Article
- Comparative modeling and benchmarking data sets for human histone deacetylases and sirtuin families.Journal of chemical information and modeling · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the "artificial enrichment" and "analogue bias" of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD.
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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.