Multi-algorithm and multi-model based drug target prediction and web server  

作　　者：Ying-tao LIU Yi LI Zi-fu HUANG Zhi-jian XU Zhuo YANG Zhu-xi CHEN Kai-xian CHEN Ji-ye SHI Wei-lia ng ZHU 

机构地区：[1]Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy ofSciences, Shanghai 201203, China [2]Informatics Department, UCB Pharma, 216 Bath Road, Slough SL1 4EN, UK

出　　处：《Acta Pharmacologica Sinica》2014年第3期419-431,共13页中国药理学报（英文版）

摘　　要：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.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.

关 键 词：drug target protein sequence multi-algorithm and multi-model strategy web server support vector machine NEURALNETWORK decision tree 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TP311.13[自动化与计算机技术—计算机科学与技术]