Research Progress of Drug Target Interaction Prediction Based on Machine Learning

doi:10.13523/j.cb.2111037

China Biotechnology

2022, Vol. 42

Issue (4): 40-48 DOI: 10.13523/j.cb.2111037

Research Progress of Drug Target Interaction Prediction Based on Machine Learning

LIU Hao-miao,YANG Zhi-wei^**(

),WANG Li-zhuo,ZHOU Yan-zhang,LONG Jian-gang

Center of Mitochondrial Biology and Medicine, Key Laboratory of Biomedical Information Engineering, Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China

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Abstract

In recent years, with the continuous breakthrough of computer hardware capability, software efficiency and data abundance, the applications of artificial intelligence technology represented by machine learning have been continuously expanded and integrated, which has greatly promoted the development in fields of biology, medicine, pharmacy, and especially drug R&D. Among those technology advancements, the identification of drug-target interactions (DTI) is an important problem in the field of drug R&D and a popular research direction for the cross-integration of artificial intelligence technology. As the source of innovative drug development, drug-target interaction prediction can provide high-probability potential drug targets for biological experiments, thereby increasing the rate of lead compound discovery, increasing the success rate of late-stage drug development and shortening the total development cycle. Researchers have already done a lot of work in constructing the prediction methods of drug-target interactions by building databases, developing software and establishing machine learning algorithms. In most works, data are transformed into feature vectors or similarities, and then suitable machine learning methods are employed to build predictive models. This paper introduces the basic process and reviews the research progress of drug-target interaction prediction based on machine learning. In addition, the advantages and disadvantages of existing prediction methods are briefly summarized in order to facilitate the development of more efficient prediction algorithms and drug-target interaction prediction methods.

Key words： Machine learning Drug target interaction Drug research Algorithm

Received: 18 November 2021 Published: 05 May 2022

ZTFLH:

Q819

Corresponding Authors: Zhi-wei YANG E-mail: yzws-123@xjtu.edu.cn

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	Hao-miao LIU
	Zhi-wei YANG
	Li-zhuo WANG
	Yan-zhang ZHOU
	Jian-gang LONG

Cite this article:

LIU Hao-miao,YANG Zhi-wei,WANG Li-zhuo,ZHOU Yan-zhang,LONG Jian-gang. Research Progress of Drug Target Interaction Prediction Based on Machine Learning. China Biotechnology, 2022, 42(4): 40-48.

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https://manu60.magtech.com.cn/biotech/10.13523/j.cb.2111037 OR https://manu60.magtech.com.cn/biotech/Y2022/V42/I4/40

Fig.1 Technology roadmap of machine learning applied in the DTI prediction

Table 2 Tools for calculating drug protein descriptors

Fig.2 Binary label matrix R


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