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学科主题: 药学
题名:
An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application To GPCRs
作者: Xia, Jie1,2; Jin, Hongwei1; Liu, Zhenming1; Zhang, Liangren1; Wang, Xiang Simon2
刊名: JOURNAL OF CHEMICAL INFORMATION AND MODELING
发表日期: 2014-05-01
DOI: 10.1021/ci500062f
卷: 54, 期:5, 页:1433-1450
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology
类目[WOS]: Chemistry, Medicinal ; Chemistry, Multidisciplinary ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
研究领域[WOS]: Pharmacology & Pharmacy ; Chemistry ; Computer Science
关键词[WOS]: PROTEIN-COUPLED RECEPTORS ; DRUG DISCOVERY ; STRUCTURAL BIOLOGY ; DECOY SETS ; DOCKING ; SELECTIVITY ; PUBCHEM ; TOOL ; IDENTIFICATION ; METHODOLOGIES
英文摘要:

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.

语种: 英语
所属项目编号: P30AI087714 ; 5P30A10877714-02 ; G12MD007597
项目资助者: District of Columbia Developmental Center for AIDS Research ; National Institutes of Health Administrative Supplements for U.S.-China Biomedical Collaborative Research ; National Institute on Minority Health and Health Disparities of the National Institutes of Health
WOS记录号: WOS:000336637400015
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.bjmu.edu.cn/handle/400002259/61838
Appears in Collections:北京大学药学院_期刊论文

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作者单位: 1.Peking Univ, Sch Pharmaceut Sci, State Key Lab Nat & Biomimet Drugs, Beijing 100191, Peoples R China
2.Howard Univ, Mol Modeling & Drug Discovery Core Dist Columbia, Lab Cheminfomat & Drug Design, Dept Pharmaceut Sci,Coll Pharm, Washington, DC 20059 USA

Recommended Citation:
Xia, Jie,Jin, Hongwei,Liu, Zhenming,et al. An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application To GPCRs[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2014,54(5):1433-1450.
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