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 language | English (US) |
|---|---|
| Pages (from-to) | 2650-2662 |
| Number of pages | 13 |
| Journal | ACS synthetic biology |
| Volume | 12 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 15 2023 |
Keywords
- bioactivity prediction
- biosynthetic gene cluster
- genome mining
- machine learning
- model construction
- natural product
ASJC Scopus subject areas
- Biomedical Engineering
- Biochemistry, Genetics and Molecular Biology (miscellaneous)