A review on compound-protein interaction prediction methods: Data, format, representation and model

Sangsoo Lim, Yijingxiu Lu, Chang Yun Cho, Inyoung Sung, Jungwoo Kim, Youngkuk Kim, Sungjoon Park, Sun Kim

Research output: Contribution to journalReview articlepeer-review

74 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1541-1556
Number of pages16
JournalComputational and Structural Biotechnology Journal
Volume19
DOIs
StatePublished - Jan 2021

Keywords

  • Chemical descriptors
  • Compound-protein interaction
  • Data representation
  • Deep learning
  • Interpretable learning
  • Machine learning
  • Pharmacophore discovery
  • Protein descriptors

Fingerprint

Dive into the research topics of 'A review on compound-protein interaction prediction methods: Data, format, representation and model'. Together they form a unique fingerprint.

Cite this