A Brief Review of Machine Learning-Based Bioactive Compound Research

Applied Sciences

Bo Ram Beck¹  Hoo Hyun Kim²  Keunsoo Kang²  Jihye Park²  Sangbum Lee³ 

[1] Deargen Inc., 193, Munji-ro, Yuseong-gu, Daejeon 34051, Korea;Department of Microbiology, College of Science & Technology, Dankook University, Cheonan 31116, Korea;Department of Software, College of Software Convergence, Dankook University, Yongin 16890, Korea;

关键词: bioactive compound;    natural product;    machine learning;    bioinformatics;    cheminformatics;    chemical space;   

DOI  :  10.3390/app12062906

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown