计算机科学
机器学习
药物靶点
人工智能
相似性(几何)
鉴定(生物学)
药物发现
深度学习
药品
计算生物学
药物开发
支持向量机
数据挖掘
药物重新定位
人工神经网络
生物信息学
医学
图像(数学)
植物
药理学
生物
作者
Wen Zhang,Weiran Lin,Ding Zhang,Siman Wang,Jingwen Shi,Yanqing Niu
出处
期刊:Current Drug Metabolism
[Bentham Science]
日期:2019-05-22
卷期号:20 (3): 194-202
被引量:41
标识
DOI:10.2174/1389200219666180821094047
摘要
Background: The identification of drug-target interactions is a crucial issue in drug discovery. In recent years, researchers have made great efforts on the drug-target interaction predictions, and developed databases, software and computational methods. Results: In the paper, we review the recent advances in machine learning-based drug-target interaction prediction. First, we briefly introduce the datasets and data, and summarize features for drugs and targets which can be extracted from different data. Since drug-drug similarity and target-target similarity are important for many machine learning prediction models, we introduce how to calculate similarities based on data or features. Different machine learningbased drug-target interaction prediction methods can be proposed by using different features or information. Thus, we summarize, analyze and compare different machine learning-based prediction methods. Conclusion: This study provides the guide to the development of computational methods for the drug-target interaction prediction.
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