Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.drudis.2007.02.015
Title: Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness
Authors: Han, L.Y. 
Zheng, C.J. 
Xie, B.
Jia, J.
Ma, X.H. 
Zhu, F. 
Lin, H.H.
Chen, X. 
Chen, Y.Z. 
Issue Date: Apr-2007
Citation: Han, L.Y., Zheng, C.J., Xie, B., Jia, J., Ma, X.H., Zhu, F., Lin, H.H., Chen, X., Chen, Y.Z. (2007-04). Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. Drug Discovery Today 12 (7-8) : 304-313. ScholarBank@NUS Repository. https://doi.org/10.1016/j.drudis.2007.02.015
Abstract: Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets. © 2007 Elsevier Ltd. All rights reserved.
Source Title: Drug Discovery Today
URI: http://scholarbank.nus.edu.sg/handle/10635/102552
ISSN: 13596446
DOI: 10.1016/j.drudis.2007.02.015
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