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  AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria

Wu, T., Zhou, M., Zou, J., Chen, Q., Qian, F., Kurths, J., Liu, R., Tang, Y. (2024): AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria. - Nature Communications, 15, 6288.
https://doi.org/10.1038/s41467-024-50533-4

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https://github.com/TianyuWu813/polymer_generation (Ergänzendes Material)
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 Urheber:
Wu, Tianyu1, Autor
Zhou, Min1, Autor
Zou, Jingcheng1, Autor
Chen, Qi1, Autor
Qian, Feng1, Autor
Kurths, Jürgen2, Autor              
Liu, Runhui1, Autor
Tang, Yang1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers (<102), much smaller than public polymer datasets (>105), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking β-amino acid polymers, we successfully simulate predictions of over 105 polymers and identify 83 optimal polymers. Furthermore, we synthesize an optimal polymer DM0.8iPen0.2 and find that this polymer exhibits broad-spectrum and potent antibacterial activity against multiple clinically isolated antibiotic-resistant pathogens, validating the effectiveness of AI-guided design strategy.

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Sprache(n): eng - Englisch
 Datum: 2024-07-262024-07-26
 Publikationsstatus: Final veröffentlicht
 Seiten: 22
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1038/s41467-024-50533-4
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
MDB-ID: No MDB - stored outside PIK (see locators/paper)
OATYPE: Gold Open Access
 Art des Abschluß: -

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Titel: Nature Communications
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, oa
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Seiten: - Band / Heft: 15 Artikelnummer: 6288 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals354
Publisher: Nature