Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness.

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.

journal_name

Drug Discov Today

journal_title

Drug discovery today

authors

Han LY,Zheng CJ,Xie B,Jia J,Ma XH,Zhu F,Lin HH,Chen X,Chen YZ

doi

10.1016/j.drudis.2007.02.015

subject

Has Abstract

pub_date

2007-04-01 00:00:00

pages

304-13

issue

7-8

eissn

1359-6446

issn

1878-5832

pii

S1359-6446(07)00096-7

journal_volume

12

pub_type

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