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Szymczak, P. ; Zarzecki, W.* ; Wang, J.* ; Duan, Y.* ; Coelho, L.P.* ; de la Fuente-Nunez, C.* ; Szczurek, E.

AI-driven antimicrobial peptide discovery: Mining and generation.

Acc. Chem. Res. 58, 1831−1846 (2025)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
ConspectusThe escalating threat of antimicrobial resistance (AMR) poses a significant global health crisis, potentially surpassing cancer as a leading cause of death by 2050. Traditional antibiotic discovery methods have not kept pace with the rapidly evolving resistance mechanisms of pathogens, highlighting the urgent need for novel therapeutic strategies. In this context, antimicrobial peptides (AMPs) represent a promising class of therapeutics due to their selectivity toward bacteria and slower induction of resistance compared to classical, small molecule antibiotics. However, designing effective AMPs remains challenging because of the vast combinatorial sequence space and the need to balance efficacy with low toxicity. Addressing this issue is of paramount importance for chemists and researchers dedicated to developing next-generation antimicrobial agents.Artificial intelligence (AI) presents a powerful tool to revolutionize AMP discovery. By leveraging AI, we can navigate the immense sequence space more efficiently, identifying peptides with optimal therapeutic properties. This Account explores the emerging application of AI in AMP discovery, focusing on two primary strategies: AMP mining, and AMP generation, as well as the use of discriminative methods as a valuable toolbox.AMP mining involves scanning biological sequences to identify potential AMPs. Discriminative models are then used to predict the activity and toxicity of these peptides. This approach has successfully identified numerous promising candidates, which were subsequently validated experimentally, demonstrating the potential of AI in AMP design and discovery.AMP generation, on the other hand, creates novel peptide sequences by learning from existing data through generative modeling. This class of models optimizes for desired properties, such as increased activity and reduced toxicity, potentially producing synthetic peptides that surpass naturally occurring ones. Despite the risk of generating unrealistic sequences, generative models hold the promise of accelerating the discovery of highly effective and highly novel and diverse AMPs.In this Account, we describe the technical challenges and advancements in these AI-based approaches. We discuss the importance of integrating various data sources and the role of advanced algorithms in refining peptide predictions. Additionally, we highlight the future potential of AI to not only expedite the discovery process but also to uncover peptides with unprecedented properties, paving the way for next-generation antimicrobial therapies.In conclusion, the synergy between AI and AMP discovery opens new frontiers in the fight against AMR. By harnessing the power of AI, we can design novel peptides that are both highly effective and safe, offering hope for a future where AMR is no longer a looming threat. Our paper underscores the transformative potential of AI in drug discovery, advocating for its continued integration into biomedical research.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Language
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 0001-4842
e-ISSN 1520-4898
Zeitschrift Accounts of Chemical Research
Quellenangaben Band: 58, Heft: 12, Seiten: 1831−1846 Artikelnummer: , Supplement: ,
Verlag American Chemical Society (ACS)
Verlagsort 1155 16th St, Nw, Washington, Dc 20036 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Human-Centered AI (HCA)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540012-001
Förderungen Defense Threat Reduction Agency (DTRA)
European Research Council (ERC) under the European Funding Union's Horizon 2020 research and innovation programme
Australian Research Council
Program of the Beijing Natural Science Foundation
AIChE Foundation
IADR Innovation in Oral Care Award
Procter & Gamble Company, United Therapeutics
BBRF Young Investigator Grant
Nemirovsky Prize
Perelman School of Medicine at the University of Pennsylvania
National Institute of General Medical Sciences of the National Institutes of Health
National Institute of General Medical Sciences
DOI 10.1021/acs.accounts.0c00594
WOS ID 001501783000001
Scopus ID 105007460640
PubMed ID 40459283
Erfassungsdatum 2025-06-04
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