Accelerating antibiotic discovery through artificial intelligence
Open accessAbstract
By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.
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AI has been successfully applied in the discovery of new antibiotics.
“AI has also been successfully applied in the discovery of new antibiotics [24].”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [24]
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An automated approach to the quantitation of vocalizations and vocal learning in the songbird
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Improving data sharing to increase the efficiency of antibiotic R&D
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Multi-label Learning for Predicting the Activities of Antimicrobial Peptides
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Flagged first, then by citations
Research works printed but not linked: 1
- [102]Goodfellow, I. et al. Generative adversarial nets. In Proc. 2014 Advances in Neural Information Processing Systems 2672–2680 (2014).
Cited by
Sorted by citations
The role of AI in drug discovery: challenges, opportunities, and strategies
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Scientific novelty beyond the experiment
Hallsworth, J. E., Udaondo, Z., Pedrós‐Alió, C., Höfer, J., Benison, K. C., Lloyd, K. G., Cordero, R. J. B., de Campos, C. B. L., Yakimov, M. M., & Amils, R. (2023) · Microbial Biotechnology · doi:10.1111/1751-7915.14222 · Cited by 62 · see citing works
- Authors
- Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C.
- Affiliations
- University of Pennsylvania (US)
- Venue
- Communications Biology
- Published
- 2021-09-09
- Volume
- 4
- Issue
- 1
- Pages
- 1050-1050
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC8429579
- OpenAlex
- W3197063311
- Cited by
- 234
- References
- 208
- Citations by year
- 2026: 44 · 2025: 60 · 2024: 65 · 2023: 42 · 2022: 20 · 2021: 2
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Alfred P. Sloan Foundation
- University of Pennsylvania
- Penn Mental Health AIDS Research Center
- National Institutes of Health
- Defense Threat Reduction Agency (HDTRA11810041, HDTRA12110014, R35GM138201, HDTRA1)
- Perelman School of Medicine, University of Pennsylvania
- National Institute of General Medical Sciences (HDTRA11810041, HDTRA1-21-1-0014, R35GM138201)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Pharmacology › Microbial Natural Products and Biosynthesis
- Life Sciences › Immunology and Microbiology › Microbiology › Antimicrobial Peptides and Activities
- Keywords
- antibiotic discovery, artificial intelligence, antimicrobial resistance, antimicrobial peptides, small-molecule antibiotics, computer-aided drug design, antimicrobial activity prediction, drug-likeness, de novo molecular design, open science, reproducibility, host-pathogen arms race
- MeSH terms
- Machine Learning, Antimicrobial Peptides, Anti-Bacterial Agents, Artificial Intelligence, Drug Design, Reproducibility of Results, Drug Discovery
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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