TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models
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Drug–target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug–drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user’s molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75–100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug–drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.
KeywordsWeb server SAR models Drug–target interaction Multi-target SAR Naïve Bayes
We would like to thank the Django group for their great Django server. We would also like to thank Dr. Peter Ertl for his JME molecular editor, and we thank the developers of D3.js. We would also like to thank three anonymous referees and the editor for their constructive comments, which greatly helped improve upon the original version of the manuscript.
This work has been financially supported by grants from the Project of Innovation-driven Plan in Central South University, the National Natural Science Foundation of China (Grants No. 81402853), the National key basic research program (Grants No. 2015CB910700), and the Postdoctoral Science Foundation of Central South University, the Chinese Postdoctoral Science Foundation (2014T70794, 2014M562142). The studies meet with the approval of the university’s review board.
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Conflict of interest
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