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Drugtarget interaction prediction: databases, web servers and computational models

Drugtarget interaction prediction: databases, web servers and computational models Identification of drugtarget interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drugtarget interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drugtarget associations on a large scale. In this review, databases and web servers involved in drugtarget identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drugtarget 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 drugtarget 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 drugtarget interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Briefings in Bioinformatics Oxford University Press

Drugtarget interaction prediction: databases, web servers and computational models

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References (88)

Publisher
Oxford University Press
Copyright
The Author 2015. Published by Oxford University Press. For Permissions, please email: [email protected]
ISSN
1467-5463
eISSN
1477-4054
DOI
10.1093/bib/bbv066
pmid
26283676
Publisher site
See Article on Publisher Site

Abstract

Identification of drugtarget interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drugtarget interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drugtarget associations on a large scale. In this review, databases and web servers involved in drugtarget identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drugtarget 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 drugtarget 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 drugtarget interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.

Journal

Briefings in Bioinformatics – Oxford University Press

Published: Jul 13, 2016

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