期刊
BRIEFINGS IN BIOINFORMATICS
卷 17, 期 4, 页码 696-712出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbv066
关键词
drug-target interactions prediction; drug discovery; computational models; biological networks; machine learning
资金
- National Natural Science of Foundation of China [11301517, 61472203, 61327902]
- foundation from National Center for Mathematics and Interdisciplinary Sciences, CAS
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology
Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target 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 drug-target 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 drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.
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