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

10.1016/j.jmgm.2007.12.002

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Bibliographic Details
Main Authors:	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.
Other Authors:	PHARMACY
Format:	Article
Published:	2014
Subjects:	Computer aided dug design Drug discovery High-throughput screening Lead discovery Machine learning method Virtual screening
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/105604
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-105604
record_format	dspace
spelling	sg-nus-scholar.10635-1056042023-10-26T08:59:20Z 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. PHARMACY Computer aided dug design Drug discovery High-throughput screening Lead discovery Machine learning method Virtual screening 10.1016/j.jmgm.2007.12.002 Journal of Molecular Graphics and Modelling 26 8 1276-1286 JMGMF 2014-10-29T01:47:58Z 2014-10-29T01:47:58Z 2008-06 Article 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. (2008-06). 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. Journal of Molecular Graphics and Modelling 26 (8) : 1276-1286. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jmgm.2007.12.002 10933263 http://scholarbank.nus.edu.sg/handle/10635/105604 000256271600009 Scopus
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Computer aided dug design Drug discovery High-throughput screening Lead discovery Machine learning method Virtual screening
spellingShingle	Computer aided dug design Drug discovery High-throughput screening Lead discovery Machine learning method Virtual screening 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. 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
description	10.1016/j.jmgm.2007.12.002
author2	PHARMACY
author_facet	PHARMACY 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.
format	Article
author	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.
author_sort	Han, L.Y.
title	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
title_short	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
title_full	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
title_fullStr	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
title_full_unstemmed	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
title_sort	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
publishDate	2014
url	http://scholarbank.nus.edu.sg/handle/10635/105604
_version_	1781788075110694912

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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