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Review

A Brief Review of Machine Learning-Based Bioactive Compound Research

by 1,†, 2,†, 1, 3,* and 1,*
1
Department of Microbiology, College of Science & Technology, Dankook University, Cheonan 31116, Korea
2
Deargen Inc., 193, Munji-ro, Yuseong-gu, Daejeon 34051, Korea
3
Department of Software, College of Software Convergence, Dankook University, Yongin 16890, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Leonel Pereira
Appl. Sci. 2022, 12(6), 2906; https://doi.org/10.3390/app12062906
Received: 23 December 2021 / Revised: 26 February 2022 / Accepted: 9 March 2022 / Published: 11 March 2022
(This article belongs to the Special Issue Bioactive Compounds for Cardiovascular and Metabolic Diseases)
Bioactive compounds are often used as initial substances for many therapeutic agents. In recent years, both theoretical and practical innovations in hardware-assisted and fast-evolving machine learning (ML) have made it possible to identify desired bioactive compounds in chemical spaces, such as those in natural products (NPs). This review introduces how machine learning approaches can be used for the identification and evaluation of bioactive compounds. It also provides an overview of recent research trends in machine learning-based prediction and the evaluation of bioactive compounds by listing real-world examples along with various input data. In addition, several ML-based approaches to identify specific bioactive compounds for cardiovascular and metabolic diseases are described. Overall, these approaches are important for the discovery of novel bioactive compounds and provide new insights into the machine learning basis for various traditional applications of bioactive compound-related research. View Full-Text
Keywords: bioactive compound; natural product; machine learning; bioinformatics; cheminformatics; chemical space; cardiovascular disease; metabolic disease bioactive compound; natural product; machine learning; bioinformatics; cheminformatics; chemical space; cardiovascular disease; metabolic disease
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MDPI and ACS Style

Park, J.; Beck, B.R.; Kim, H.H.; Lee, S.; Kang, K. A Brief Review of Machine Learning-Based Bioactive Compound Research. Appl. Sci. 2022, 12, 2906. https://doi.org/10.3390/app12062906

AMA Style

Park J, Beck BR, Kim HH, Lee S, Kang K. A Brief Review of Machine Learning-Based Bioactive Compound Research. Applied Sciences. 2022; 12(6):2906. https://doi.org/10.3390/app12062906

Chicago/Turabian Style

Park, Jihye, Bo R. Beck, Hoo H. Kim, Sangbum Lee, and Keunsoo Kang. 2022. "A Brief Review of Machine Learning-Based Bioactive Compound Research" Applied Sciences 12, no. 6: 2906. https://doi.org/10.3390/app12062906

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