TY - JOUR AU - Liu Ying-tao AU - Li Yi AU - Huang Zi-fu AU - Xu Zhi-jian AU - Yang Zhuo AU - Chen Zhu-xi AU - Chen Kai-xian AU - Shi Ji-ye AU - Zhu Wei-liang PY - 2016 TI - Multi-algorithm and multi-model based drug target prediction and web server JF - Acta Pharmacologica Sinica; Vol 35, No 3 (March 2014): Acta Pharmacologica Sinica Y2 - 2016 KW - N2 - Ying-tao LIU1, #, Yi LI1, #, Zi-fu HUANG1, #, Zhi-jian XU1, Zhuo YANG1, Zhu-xi CHEN1, Kai-xian CHEN1, Ji-ye SHI2, *, Wei-liang ZHU1, * 1Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; 2Informatics Department, UCB Pharma, 216 Bath Road, Slough SL1 4EN, UK Aim: 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. Conclusion: Reliable and robust drug target prediction based on protein sequences is achieved using the multi-algorithm and multi-model strategy. Keywords: drug target; protein sequence; multi-algorithm and multi-model strategy; web server; support vector machine; neural network; decision tree This work was supported by National Natural Science Foundation of China (81273435 and 21021063), National Science & Technology Projects (2012ZX09301001-004, 2012AA01A305, and 2013ZX09103001-001). Computational resources were provided by supercomputer TianHe-I in Tianjin and the Shanghai Supercomputing Center (SCC). The authors thank the developers of free and/or open source software for academic use, including SignalP-3.0, netOglyc-3.1d, netNglyc-1.0, tmhmm-2.0c and EMBOSS-6.0.1. # The first three authors contributed equally to this work. * To whom correspondence should be addressed. E-mail Jiye.Shi@ucb.com (Ji-ye SHI); wlzhu@mail.shcnc.ac.cn (Wei-liang ZHU) Received 2013-07-20 Accepted 2013-09-23 UR - http://www.chinaphar.com/article/view/7718