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Cesar de la Fuente-Nunez1,2,3,4, James J Collins5,6,7

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Machine learning and artificial intelligence accelerate the discovery of new antibiotics to combat antimicrobial resistance, a major global health threat. These advanced computational methods offer a faster, more efficient alternative to traditional drug discovery approaches.

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Area of Science:

  • Microbiology
  • Computational Biology
  • Drug Discovery

Background:

  • Antimicrobial resistance (AMR) is a critical global health threat, identified by the World Health Organization as a top public health concern.
  • The rise of drug-resistant pathogens risks a post-antibiotic era, rendering common infections potentially fatal.
  • Traditional antibiotic discovery methods are slow, costly, and inadequate against rapidly evolving resistance.

Purpose of the Study:

  • To discuss the role of machine learning (ML) and artificial intelligence (AI) in accelerating antibiotic discovery.
  • To explore the potential and challenges of ML/AI in combating antimicrobial resistance.
  • To speculate on the future evolution of this field, including contributions from physics.

Main Methods:

  • Review of existing literature and insights on ML/AI applications in antibiotic discovery.
  • Analysis of how ML and AI expedite the identification of potential antibiotic candidates.
  • Consideration of interdisciplinary contributions, particularly from the physics community.

Main Results:

  • ML and AI significantly reduce the time and cost associated with identifying novel antibiotic candidates.
  • These technologies offer a transformative alternative to conventional, slower discovery pipelines.
  • The integration of ML/AI holds promise for a more robust response to the AMR crisis.

Conclusions:

  • ML and AI are pivotal in accelerating the search for new antibiotics to address the global challenge of antimicrobial resistance.
  • The field presents significant opportunities and challenges, with potential for future advancements and interdisciplinary collaboration.
  • Continued innovation in computational approaches, including physics-informed methods, is crucial for future success in antibiotic discovery.