Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness.
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.
Han
Lian Yi
LY
Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Blk Soc 1, Level 7, 3 Science Drive 2, Singapore 117543.
Zheng
Chan Juan
CJ
Xie
Bin
B
Jia
Jia
J
Ma
Xiao Hua
XH
Zhu
Feng
F
Lin
Hong Huang
HH
Chen
Xin
X
Chen
Yu Zong
YZ
D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D015195:Drug Design; D006801:Humans; D008962:Models, Theoretical; D016415:Sequence Alignment / Q000379:methods*