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