TargetNet: Target netting, rather than angling

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. In drug discovery process, one of the challenges is to identify the potential targets for drug-like compounds. Once the target is successfully identified, several receptor-based drug design methods could be easily used to optimize the structure of compounds, aims at improving the biological activity of these compounds.

However, this is usually a very difficult task for most of medicinal chemists. Furthermore, 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. Herein, we attempted to address the difficulty by developing a web server called TargetNet.

2TargetNet is an open web server that could be used for netting or predicting the binding of multiple targets for any given molecule. TargetNet simultaneously constructs a large number of QSAR models based on current chemogenomics data to make future predictions. When the user submits a molecule, the server will predict the activity of the user’s molecule across 623 human proteins by establishing the high quality QSAR model for each human protein, thus generating a DTI profiling that can used as a feature vector for wide applications.
2The 623 QSAR 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.

  Please cite

  1. Cao, D.S. et al., TargetNet: an open web server used for netting or predicting the binding of multiple targets for given molecules. Bioinformatics, submitted.
  2. Cao, D.S., et al., Computational Prediction of Drug Target Interactions Using Chemical, Biological, and Network Features. Molecular Informatics, 2014. 33(10): p. 669-681.

Copyright @ 2012-2014 Computational Biology & Drug Design Group,
School of Pharmaceutical Sciences, Central South University. All rights reserved.

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