基于机器学习的药物-靶标相互作用预测<sup>*</sup>

doi:10.13523/j.cb.2111037

中国生物工程杂志

2022, Vol. 42

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

综述

基于机器学习的药物-靶标相互作用预测^*

刘皓淼,杨志伟^**(

),王力卓,周彦章,龙建纲

西安交通大学生命科学与技术学院线粒体生物医学研究所生物医学信息工程教育部重点实验室西安 710049

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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摘要：

近年来,随着计算机硬件、软件工具和数据丰度的不断突破,以机器学习为代表的人工智能技术在生物、基础医学和药学等领域的应用不断拓展和融合,极大地推动了这些领域的发展,尤其是药物研发领域的变革。其中,药物-靶标相互作用(drug-target interactions, DTI)的识别是药物研发领域中的重要难题和人工智能技术交叉融合的热门方向,研究人员在DTI预测方面做了大量的工作,构建了许多重要的数据库,开发或拓展了各类机器学习算法和工具软件。对基于机器学习的DTI预测的基本流程进行了介绍,并对利用机器学习预测DTI的研究进行了回顾,同时对不同的机器学习方法运用于DTI预测的优缺点进行了简单总结,以期对开发更加有效的预测算法和DTI预测的发展提供帮助。

关键词： 机器学习; 药物-靶标相互作用; 药物研发; 算法

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

收稿日期: 2021-11-18 出版日期: 2022-05-05

ZTFLH:

Q819

基金资助: * 国家自然科学基金(31870848);陕西省科学基金重点项目(2018JZ3005)

通讯作者: 杨志伟 E-mail: yzws-123@xjtu.edu.cn

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引用本文:

刘皓淼,杨志伟,王力卓,周彦章,龙建纲. 基于机器学习的药物-靶标相互作用预测^*[J]. 中国生物工程杂志, 2022, 42(4): 40-48.

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.

链接本文:

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

图1 机器学习应用于预测DTI技术路线图

表1 常用数据库

表2 用于计算药物蛋白质描述符的工具

图2 二进制标签矩阵R

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