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Title: Identifying novel type ZBGs and nonhydroxamate HDAC inhibitors through a SVM based virtual screening approach
Authors: Liu, X.H.
Song, H.Y.
Zhang, J.X.
Han, B.C.
Wei, X.N.
Ma, X.H. 
Cui, W.K.
Chen, Y.Z. 
Keywords: Computer aided drug design
Drug design
In silico modeling
Machine learning
Support vector machine
Virtual screening
Zinc binding group
Issue Date: 17-May-2010
Citation: Liu, X.H., Song, H.Y., Zhang, J.X., Han, B.C., Wei, X.N., Ma, X.H., Cui, W.K., Chen, Y.Z. (2010-05-17). Identifying novel type ZBGs and nonhydroxamate HDAC inhibitors through a SVM based virtual screening approach. Molecular Informatics 29 (5) : 407-420. ScholarBank@NUS Repository.
Abstract: Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design. © 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Source Title: Molecular Informatics
ISSN: 18681743
DOI: 10.1002/minf.200900014
Appears in Collections:Staff Publications

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