New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning

With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and state-of-the-art of antibiotic discovery based on ecological principles, genome mining and artificial intelligence.

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Publication Details

Journal
PLoS Biology
Published
2025-02-28
DOI
https://doi.org/10.1371/journal.pbio.3003058
Citations
10
Primary Topic
Microbial Natural Products and Biosynthesis
Type
article
Field-Weighted Citation Impact
13.94
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article

New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning

Marnix H. Medema, Gilles P. van Wezel
10 citations
PLoS Biology
Microbial Natural Products and Biosynthesis
13.94
article

New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning

Marnix H. Medema, Gilles P. van Wezel
article en
10 citations

Abstract

With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and state-of-the-art of antibiotic discovery based on ecological principles, genome mining and artificial intelligence.

PLoS BiologyVol. 23(2)
Leiden University (NL), Wageningen University & Research (NL)
Responsible consumption and production
Openalex Percentile: Top 1%
Microbial Natural Products and Biosynthesis
13.94
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