Machine Learning Accelerates De Novo Design of Antimicrobial Peptides
Author:
Funder
Natural Science Foundation of Henan Province
Innovative Funds Plan of Henan University of Technology
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12539-024-00612-3.pdf
Reference29 articles.
1. United Nations Environment Programme (2023) Bracing for Superbugs: Strengthening environmental action in the One Health response to antimicrobial resistance. Geneva. https://www.unep.org/resources/superbugs/environmental-action. Accessed 5 Mar 2023
2. Huemer M, Shambat SM, Brugger SD et al (2020) Antibiotic resistance and persistence—implications for human health and treatment perspectives. EMBO Rep 21(12):51034. https://doi.org/10.15252/embr.202051034
3. Hafeez AB, Jiang X, Bergen PJ et al (2021) Antimicrobial Peptides: an update on classifications and databases. Int J Mol Sci 22(21):11691. https://doi.org/10.3390/ijms222111691
4. Luo Y, Song Y (2021) Mechanism of antimicrobial peptides: antimicrobial, anti-inflammatory and antibiofilm activities. Int J Mol Sci 22(21):11401. https://doi.org/10.3390/ijms222111401
5. Mahlapuu M, Håkansson J, Ringstad L et al (2016) Antimicrobial peptides: an emerging category of therapeutic agents. Front Cell Infect Microbiol 6:194. https://doi.org/10.3389/fcimb.2016.00194
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