Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products

Yujie Yuan, Chengyou Shi, Huimin Zhao

Research output: Contribution to journalArticlepeer-review

Abstract

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

Original languageEnglish (US)
Pages (from-to)2650-2662
Number of pages13
JournalACS synthetic biology
Volume12
Issue number9
DOIs
StatePublished - Sep 15 2023

Keywords

  • bioactivity prediction
  • biosynthetic gene cluster
  • genome mining
  • machine learning
  • model construction
  • natural product

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Biomedical Engineering

Fingerprint

Dive into the research topics of 'Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products'. Together they form a unique fingerprint.

Cite this