ArticleMethods (San Diego, Calif.)2015
Benchmarking methods and data sets for ligand enrichment assessment in virtual screening.
Article in Methods (San Diego, Calif.), 2015. 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.
- An efficient computational chemistry approach to generating negative data for drug discovery pipeline validation.Frontiers in bioinformatics · 2026Article
- ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance.Nature communications · 2025Article
- iScore: A ML-Based Scoring Function for De Novo Drug Discovery.Journal of chemical information and modeling · 2025Article
- An overview of recent advances and challenges in predicting compound-protein interaction (CPI).Medical review (2021) · 2023Review
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- Protein-ligand binding affinity prediction with edge awareness and supervised attention.iScience · 2023Article
- Confidence bands and hypothesis tests for hit enrichment curves.Journal of cheminformatics · 2022Article
- EMBER-Embedding Multiple Molecular Fingerprints for Virtual Screening.International journal of molecular sciences · 2022Article
- Ligand-Based Virtual Screening Based on the Graph Edit Distance.International journal of molecular sciences · 2021Article
- Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design.Journal of chemical information and modeling · 2020Article
- Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening.Current topics in medicinal chemistry · 2020Article
- Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.Briefings in bioinformatics · 2019Review
- Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search.Molecules (Basel, Switzerland) · 2019Article
- Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure.Journal of chemical information and modeling · 2019Article
- Binding mode information improves fragment docking.Journal of cheminformatics · 2019Article
- Pharmacophore-based virtual screening of catechol-o-methyltransferase (COMT) inhibitors to combat Alzheimer's disease.Journal of biomolecular structure & dynamics · 2018Article
- SimCAL: a flexible tool to compute biochemical reaction similarity.BMC bioinformatics · 2018Article
- Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis.Journal of chemical information and modeling · 2018Article
- The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening.Journal of chemical information and modeling · 2017Article
- A Thoroughly Validated Virtual Screening Strategy for Discovery of Novel HDAC3 Inhibitors.International journal of molecular sciences · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. "analogue bias", "artificial enrichment" and "false negative". In addition, we introduce our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylases (HDACs) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The leave-one-out cross-validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased as measured by property matching, ROC curves and AUCs.
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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.