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Application of the Intelligent High-Throughput Antimicrobial Sensitivity Testing/Phage Screening System and Lar Index of Antimicrobial Resistance
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Harnessing AI for Antimicrobial Peptide Innovation against Multidrug Resistance.

João P F Pimentel1, Raquel M Quigua Orozco1, Samilla Beatriz de Rezende1

  • 1S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande-MS 79117900, Brazil.

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|February 27, 2026
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Summary

Artificial intelligence (AI) accelerates antimicrobial peptide (AMP) discovery for combating antimicrobial resistance (AMR). AI models predict, design, and optimize novel AMPs, enhancing drug development pipelines.

Keywords:
antimicrobial peptidesartificial intelligencedeep learningmachine learningpeptide-based drugs

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Drug Discovery

Background:

  • Antimicrobial resistance (AMR) is a major global health crisis requiring new therapeutic strategies.
  • Antimicrobial peptides (AMPs) show promise as alternatives to conventional antibiotics but are challenging to discover experimentally.
  • Artificial intelligence (AI) offers powerful tools to overcome limitations in AMP identification and development.

Purpose of the Study:

  • To review recent advancements in AI-driven approaches for antimicrobial peptide discovery.
  • To highlight the role of machine learning (ML), deep learning (DL), and generative models in accelerating AMP research.
  • To discuss the integration of multiomics data and emerging technologies like quantum computing (QC) in peptide design.

Main Methods:

  • Utilizing predictive models for accurate AMP identification and screening.
  • Employing generative models for the design and optimization of novel AMP candidates.
  • Integrating multiomics data to enhance understanding of peptide function and mechanisms.
  • Exploring quantum computing (QC) to address computational challenges in peptide design.

Main Results:

  • AI significantly accelerates the large-scale screening and functional annotation of potential AMPs.
  • AI enables accurate prediction and design of novel antimicrobial peptides.
  • Integration of multiomics data provides deeper insights into AMP efficacy.
  • Emerging technologies like QC show potential for advanced peptide design.

Conclusions:

  • AI-driven strategies are revolutionizing antimicrobial peptide discovery.
  • These advanced computational approaches are crucial for developing next-generation peptide-based antimicrobials.
  • AI integration promises to expand the therapeutic landscape and address urgent clinical needs in combating AMR.