title: Benchmarking methods and data sets for ligand enrichment assessment in virtual screening.
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.
authors.affiliation: State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, PR China; Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA.
authors.forename: Jie
authors.initials: J
authors.lastname: Xia
authors.affiliation: Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA.
authors.forename: Ermias Lemma
authors.initials: EL
authors.lastname: Tilahun
authors.affiliation: Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA.
authors.forename: Terry-Elinor
authors.initials: TE
authors.lastname: Reid
authors.affiliation: State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, PR China. Electronic address: liangren@bjmu.edu.cn.
authors.forename: Liangren
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA. Electronic address: x.simon.wang@gmail.com.
authors.forename: Xiang Simon
authors.initials: XS
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D019985:Benchmarking; D055808:Drug Discovery / Q000379:methods; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008024:Ligands; D012372:ROC Curve
chemical_list: D008024:Ligands
keywords: Analogue bias; Artificial enrichment; Benchmarking methodology; Decoy sets; Ligand-based virtual screening; Structure-based virtual screening