Journal of Computer-Aided Molecular Design

, Volume 30, Issue 5, pp 413–424

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

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

DOI: 10.1007/s10822-016-9915-2

Cite this article as:
Yao, ZJ., Dong, J., Che, YJ. et al. J Comput Aided Mol Des (2016) 30: 413. doi:10.1007/s10822-016-9915-2

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.

Keywords

Web server SAR models Drug–target interaction Multi-target SAR Naïve Bayes 

Supplementary material

10822_2016_9915_MOESM1_ESM.xls (101 kb)
Supplementary material 1 (XLS 101 kb)
10822_2016_9915_MOESM2_ESM.xls (199 kb)
Supplementary material 2 (XLS 199 kb)
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Supplementary material 3 (XLS 1844 kb)
10822_2016_9915_MOESM4_ESM.xls (534 kb)
Supplementary material 4 (XLS 534 kb)

Funding information

Funder NameGrant NumberFunding Note
the Project of Innovation-driven Plan in Central South University, the National Natural Science Foundation of China
  • 81402853
the National key basic research program
  • 2015CB910700
the Postdoctoral Science Foundation of Central South University, the Chinese Postdoctoral Science Foundation
  • 2014T70794
  • 2014M562142

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhi-Jiang Yao
    • 1
    • 2
  • Jie Dong
    • 1
  • Yu-Jing Che
    • 3
  • Min-Feng Zhu
    • 3
  • Ming Wen
    • 2
  • Ning-Ning Wang
    • 1
  • Shan Wang
    • 2
  • Ai-Ping Lu
    • 4
  • Dong-Sheng Cao
    • 1
    • 4
  1. 1.School of Pharmaceutical SciencesCentral South UniversityChangshaPeople’s Republic of China
  2. 2.College of Chemistry and Chemical EngineeringCentral South UniversityChangshaPeople’s Republic of China
  3. 3.School of Mathematics and StatisticsCentral South UniversityChangshaPeople’s Republic of China
  4. 4.Institute of Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese MedicineHong Kong Baptist UniversityHong KongPeople’s Republic of China

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