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Generative AI in microbial evolution and resistance: toward robust, explainable, and equitable predictions.

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  • 1COEUS Institute, New Market, VA, United States.

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Summary

Generative artificial intelligence (AI) offers new ways to combat antimicrobial resistance (AMR). Addressing evolutionary robustness, explainability, and data equity is crucial for AI to effectively support drug discovery and surveillance.

Keywords:
GPT in medical domainantimicrobial resistancedrug discoveryexplainability and biosafetyexplainable AIgenerative artificial intelligencemicrobial evolution

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Area of Science:

  • Microbiology and Evolutionary Biology
  • Artificial Intelligence and Machine Learning
  • Global Health

Background:

  • Antimicrobial resistance (AMR) is a critical global health crisis exacerbated by clinical, ecological, and social factors.
  • Evolutionary dynamics play a key role in the development and spread of AMR.
  • Existing approaches to AMR lack predictive power regarding evolutionary pathways.

Purpose of the Study:

  • To explore the potential of generative AI and large language models (LLMs) in addressing the AMR challenge.
  • To outline essential considerations for the effective integration of AI in AMR research and intervention.
  • To propose a framework for developing evolution-aware, transparent, and equitable AI systems for microbiology.

Main Methods:

  • This perspective synthesizes current knowledge on AMR, evolutionary dynamics, and AI capabilities.
  • It identifies key imperatives for developing robust and reliable AI models for AMR.
  • The authors propose a conceptual framework for generative AI as an epistemic infrastructure.

Main Results:

  • Generative AI and LLMs can anticipate resistance, design novel antimicrobials, and guide interventions.
  • Three core imperatives for AI integration are identified: evolutionary robustness, explainability/biosafety, and data equity.
  • Addressing these imperatives will enable AI to support sustainable drug discovery and adaptive surveillance.

Conclusions:

  • Generative AI must be developed as an evolution-aware, transparent, and globally inclusive infrastructure.
  • Successful AI integration requires models that predict evolutionary trajectories and ensure biological credibility.
  • Prioritizing data equity is essential for AI systems to effectively serve populations most impacted by AMR.