ReviewFrontiers in pharmacology2018
Decoys Selection in Benchmarking Datasets: Overview and Perspectives.
Review in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed.
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- VsNsbench: evaluating AlphaFold3-embed induced-fit mechanism for enhanced virtual screening.Acta pharmacologica Sinica · 2026Article
- Structure-Guided Repurposing of Approved Drugs Identifies Aprepitant and Mavorixafor as Putative δ-Opioid Receptor Agonist Candidates.International journal of molecular sciences · 2026Article
- Structure- and Ligand-Based Discovery of Novel 3-Chymotrypsin-Like Protease Nonpeptidomimetic Hits.ChemMedChem · 2026Article
- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
- Multicomplex Pharmacophore Modeling of Estrogen Receptors Suggests the Probable Repurposing of Procaterol as an Antiproliferative Agent Against Breast Cancer Cells.International journal of molecular sciences · 2026Article
- High-Throughput, High-Quality: Benchmarking GNINA and AutoDock Vina for Precision Virtual Screening Workflow.Molecules (Basel, Switzerland) · 2025Article
- InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem.Journal of cheminformatics · 2025Article
- Approaching Pharmacological Space: Events and Components.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers.Journal of advanced research · 2025Article
- Identification of novel PAD2 inhibitors using pharmacophore-based virtual screening, molecular docking, and MD simulation studies.Scientific reports · 2024Article
- Projecting phytochemical bacoside A anti-mucorale agent: An in-silico and in-vitro assessment.Heliyon · 2024Article
- Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No).Pharmaceuticals (Basel, Switzerland) · 2024Article
- The pursuit of accurate predictive models of the bioactivity of small molecules.Chemical science · 2024Review
- Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking.Future medicinal chemistry · 2024Article
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- A practical guide to machine-learning scoring for structure-based virtual screening.Nature protocols · 2023Review
- Benchmarked molecular docking integrated molecular dynamics stability analysis for prediction of SARS-CoV-2 papain-like protease inhibition by olive secoiridoids.Journal of King Saud University. Science · 2023Article
- Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening.Molecules (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.
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