Drug target interaction prediction: databases, web servers and computational models
Identification of drug target interactions is an important process in drug discovery. Although high throughput screening and other biological assays are becoming available, experimental methods for drug target interaction identification remain to be extremely costly, time consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug target associations on a large scale. In this review, databases and web servers involved in drug target identification and drug discovery are summarized. In addition, we mainly introduced some state of the art computational models for drug target interactions prediction, including network based method, machine learning based method and so on. Specially, for the machine learning based method, much attention was paid to supervised and semi supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug target interaction prediction have been obtained by many effective computational models, both network based and machine learning based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone based network and cancer hallmark based network. Finally, we discussed the new evaluation validation framework and the formulation of drug target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.
Briefings Bioinform.