A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

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	A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor
	Han, L. Y. ; Ma, X. H. ; Lin, H. H. ; Jia, J. ; Zhu, F. ; Xue, Y. ; Li, Z. R. ; Cao, Z. W. ; Ji, Z. L. ; Chen, Y. Z. ; Ji ZL(纪志梁)
刊名	http://dx.doi.org/10.1016/j.jmgm.2007.12.002
	2008-06
关键词	STATISTICAL LEARNING-METHODS HIGH-THROUGHPUT DOCKING BINARY KERNEL DISCRIMINATION DRUG DISCOVERY IN-SILICO MOLECULAR DESCRIPTORS CHEMICAL SPACE LEAD DISCOVERY NEURAL-NETWORK RECEPTOR ANTAGONISTS
英文摘要	Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >= 1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries. (C) 2007 Elsevier Inc. All rights reserved.
语种	英语
出版者	J MOL GRAPH MODEL
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/90603]
专题	生命科学－已发表论文
推荐引用方式 GB/T 7714	Han, L. Y.,Ma, X. H.,Lin, H. H.,et al. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor[J]. http://dx.doi.org/10.1016/j.jmgm.2007.12.002,2008.
APA	Han, L. Y..,Ma, X. H..,Lin, H. H..,Jia, J..,Zhu, F..,...&纪志梁.(2008).A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor.http://dx.doi.org/10.1016/j.jmgm.2007.12.002.
MLA	Han, L. Y.,et al."A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor".http://dx.doi.org/10.1016/j.jmgm.2007.12.002 (2008).