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

ABSTRACT

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.