Multi-algorithm and multi-model based drug target prediction and web server.
OBJECTIVE
To develop a reliable computational approach for predicting potential drug targets based merely on protein sequence.


METHODS
With drug target and non-target datasets prepared and 3 classification algorithms (Support Vector Machine, Neural Network and Decision Tree), a multi-algorithm and multi-model based strategy was employed for constructing models to predict potential drug targets.


RESULTS
Twenty one prediction models for each of the 3 algorithms were successfully developed. Our evaluation results showed that ∼30% of human proteins were potential drug targets, and ∼40% of putative targets for the drugs undergoing phase II clinical trials were probably non-targets. A public web server named D3TPredictor (http://www.d3pharma.com/d3tpredictor) was constructed to provide easy access.


CONCLUSIONS
Reliable and robust drug target prediction based on protein sequences is achieved using the multi-algorithm and multi-model strategy.
Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Ying-tao
YT
Liu
Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Yi
Y
Li
Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Zi-fu
ZF
Huang
Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Zhi-jian
ZJ
Xu
Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Zhuo
Z
Yang
Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Zhu-xi
ZX
Chen
Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Kai-xian
KX
Chen
Informatics Department, UCB Pharma, 216 Bath Road, Slough SL1 4EN, UK.
Ji-ye
JY
Shi
Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Wei-liang
WL
Zhu
D000465:Algorithms; D000595:Amino Acid Sequence; D017076:Computer-Aided Design; D030562:Databases, Protein; D003663:Decision Trees; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D020407:Internet; D016571:Neural Networks, Computer; D020543:Proteome; D015203:Reproducibility of Results; D013329:Structure-Activity Relationship; D060388:Support Vector Machine
D020543:Proteome