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• W2766842911 endingPage "20160153" @default.
• W2766842911 startingPage "20160153" @default.
• W2766842911 abstract "Antimicrobial peptides (AMPs) are a diverse class of well-studied membrane-permeating peptides with important functions in innate host defense. In this short review, we provide a historical overview of AMPs, summarize previous applications of machine learning to AMPs, and discuss the results of our studies in the context of the latest AMP literature. Much work has been recently done in leveraging computational tools to design new AMP candidates with high therapeutic efficacies for drug-resistant infections. We show that machine learning on AMPs can be used to identify essential physico-chemical determinants of AMP functionality, and identify and design peptide sequences to generate membrane curvature. In a broader scope, we discuss the implications of our findings for the discovery of membrane-active peptides in general, and uncovering membrane activity in new and existing peptide taxonomies." @default.
• W2766842911 created "2017-11-10" @default.
• W2766842911 creator A5005220470 @default.
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• W2766842911 creator A5044498210 @default.
• W2766842911 creator A5066307655 @default.
• W2766842911 creator A5078191755 @default.
• W2766842911 date "2017-10-20" @default.
• W2766842911 modified "2023-10-17" @default.
• W2766842911 title "What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?" @default.
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