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TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models

Overview of attention for article published in Journal of Computer-Aided Molecular Design, May 2016
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Title
TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models
Published in
Journal of Computer-Aided Molecular Design, May 2016
DOI 10.1007/s10822-016-9915-2
Pubmed ID
Authors

Zhi-Jiang Yao, Jie Dong, Yu-Jing Che, Min-Feng Zhu, Ming Wen, Ning-Ning Wang, Shan Wang, Ai-Ping Lu, Dong-Sheng Cao

Abstract

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 .

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 30%
Student > Bachelor 9 24%
Student > Master 5 14%
Researcher 4 11%
Other 3 8%
Other 5 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 24%
Chemistry 7 19%
Medicine and Dentistry 4 11%
Agricultural and Biological Sciences 4 11%
Engineering 3 8%
Other 10 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 May 2016.
All research outputs
#10,650,931
of 12,010,397 outputs
Outputs from Journal of Computer-Aided Molecular Design
#498
of 571 outputs
Outputs of similar age
#231,431
of 278,807 outputs
Outputs of similar age from Journal of Computer-Aided Molecular Design
#4
of 6 outputs
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