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Machine learning approaches for elucidating the biological effects of natural products

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Abstract

Covering: 2000 to 2020

Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure–activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.

Graphical abstract: Machine learning approaches for elucidating the biological effects of natural products

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Article information


Submitted
17 Jun 2020
First published
01 Sep 2020

Nat. Prod. Rep., 2020, Advance Article
Article type
Review Article

Machine learning approaches for elucidating the biological effects of natural products

R. Zhang, X. Li, X. Zhang, H. Qin and W. Xiao, Nat. Prod. Rep., 2020, Advance Article , DOI: 10.1039/D0NP00043D

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