文章基本信息

标题：A review on compound-protein interaction prediction methods: Data, format, representation and model
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作者：Sangsoo Lim ; Yijingxiu Lu ; Chang Yun Cho 等
期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2021
卷号：19
页码：1541-1556
DOI：10.1016/j.csbj.2021.03.004
出版社：Computational and Structural Biotechnology Journal
摘要：There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.
关键词：Compound-protein interaction ; Data representation ; Interpretable learning ; Chemical descriptors ; Protein descriptors ; Machine learning ; Deep learning ; Pharmacophore discovery