Computational approaches are emerging as powerful tools for the discovery of antibiotics. A study now uses machine learning to discover abaucin, a potent antibiotic that targets the bacterial pathogen Acinetobacter baumannii.
Traditionally, antibiotics were discovered by the screening of soil microorganisms, seeking secondary metabolites. From these initial molecules, numerous derivatives were synthesized, showing improved bioactivities and greater potential to meet clinical standards. Until recently, discovery efforts relied on the painstaking screening of large compound libraries for antibiotic activity1. Phenotypic screening approaches do not require advanced knowledge of the mechanism of action; however, the extensive and unexplored chemical space in the search for drug-like molecules poses considerable challenges, as the screening of numerous potential combinations (of both compounds and different bacterial strains) is both expensive and time-consuming. Thus, the discovery process of a new antibiotic has consistently depended on chance and serendipity. Recent advances that incorporate cutting-edge artificial intelligence (AI) and other computational approaches have demonstrated remarkable success in accelerating the identification of new drugs with desired properties2,3,4,5,6,7,8,9,10. Reporting in Nature Chemical Biology, Liu et al.11 have applied a machine learning algorithm to rapidly predict the antimicrobial activity of molecules against the Gram-negative pathogen Acinetobacter baumannii — which is a cause of nosocomial (hospital-derived) infections.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
24,99 € / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
269,00 € per year
only 22,42 € per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Additional access options:
Log in
Learn about institutional subscriptions
Read our FAQs
Contact customer support
Fig. 1: The discovery of abaucin driven by machine learning.
References
Wright, G. D. Nat. Prod. Rep.34, 694–701 (2017).
Article
CAS
PubMed
Google Scholar
Porto, W. F. et al. Nat. Commun.9, 1490 (2018).
Article
PubMed
PubMed Central
Google Scholar
Torres, M. D. T. et al. Nat. Biomed. Eng.6, 1451–1451 (2022).
Article
PubMed
Google Scholar
Cesaro, A. et al. ACS Nano.16, 1880–1895 (2022).
Article
CAS
PubMed
Google Scholar
Das, P. et al. Nat. Biomed. Eng.5, 613–623 (2021).
Article
CAS
PubMed
Google Scholar
Sberro, H. et al. Cell178, 1245–1259.e14 (2019).
Article
CAS
PubMed
PubMed Central
Google Scholar
Maasch, J. R. M. A., Torres, M. D. T., Melo, M. C. R. & de la Fuente-Nunez, C. Cell Host Microbe31, 1260–1274.e6 (2023).
Article
CAS
PubMed
Google Scholar
Ma, Y. et al. Nat. Biotechnol.40, 921–931 (2022).
Article
CAS
PubMed
Google Scholar
Wong, F., de la Fuente-Nunez, C. & Collins, J. J. Science381, 164–170 (2023).
Article
CAS
PubMed
Google Scholar
Torres M. D. T. et al. Preprint at bioRxiv https://doi.org/10.1101/2023.08.31.555711 (2023).
Liu, G. et al. Nat. Chem. Biol. https://doi.org/10.1038/s41589-023-01349-8 (2023).
Article
PubMed
PubMed Central
Google Scholar
Stokes, J. M. et al. Cell180, 688–702.e13 (2020).
Article
CAS
PubMed
PubMed Central
Google Scholar
Jin, W. et al. Proc. Natl Acad. Sci. USA118, e2105070118 (2021).
Article
CAS
PubMed
PubMed Central
Google Scholar
Preuer, K. et al. Bioinformatics34, 1538–1546 (2018).
Article
CAS
PubMed
Google Scholar
Bran, A. M., Cox, S., White, A. D. & Schwaller, P. Preprint at https://doi.org/10.48550/arXiv.2304.05376 (2023).
Download references
Author information
Authors and Affiliations
Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Angela Cesaro & Cesar de la Fuente-Nunez
Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
Angela Cesaro & Cesar de la Fuente-Nunez
Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
Angela Cesaro & Cesar de la Fuente-Nunez
Corresponding author
Ethics declarations
Competing interests
C.F.-N. provides consulting services to Invaio Sciences and is a member of the scientific advisory boards of Nowture S.L. and Phare Bio. The de la Fuentel lab has received research funding or in-kind donations from United Therapeutics, Strata Manufacturing PJSC, and Procter & Gamble, none of which were used in support of this work.
About this article
Cite this article
Cesaro, A., de la Fuente-Nunez, C. Antibiotic identified by AI.
Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01448-6
Download citation
Published: 11 October 2023
DOI: https://doi.org/10.1038/s41589-023-01448-6
>>> Read full article>>>
Copyright for syndicated content belongs to the linked Source : Hacker News – https://www.nature.com/articles/s41589-023-01448-6