Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology

Alexa Sowers, Guangshun Wang, Malcolm Xing, Bingyun Li

Research output: Contribution to journalReview articlepeer-review

9 Scopus citations


Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.

Original languageEnglish (US)
Article number1129
Issue number5
StatePublished - May 2023


  • LL-37
  • antibiotic resistance
  • antimicrobial peptide
  • drug delivery
  • machine learning
  • nanotechnology
  • peptide database
  • peptide design
  • peptide engineering

ASJC Scopus subject areas

  • Microbiology
  • Microbiology (medical)
  • Virology


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