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<p>Artificial intelligence or AI is reshaping the fight against antimicrobial resistance (AMR) by offering innovative resources across multiple domains. In this work AI resource for microbial biofilms are covered which can identify compounds that disrupt resistant microbial communities by enhancing treatment efficacy. The AI resource for AMR markers, along with resources for surveillance and monitoring of AMR are also analysed. Accelerating the design of novel antibiotics by predicting bacterial protein structures is also presented with an emphasis on AI-powered diagnostics for AMR management. The potential of AI in AMR is extensive, with opportunities for real-time monitoring, generative drug development, and portable diagnostic tools, all contributing to a potent strategy by lowering expenses and responding to the evolution of resistance. Addressing existing limitations allows for the integration of human, animal, and environmental data through AI, offering a thorough, sustainable approach to combating AMR, which stands as one of the most significant challenges to global health. This study also explore digital tools and resources such as Machine Learning (ML) and Deep Learning (DL) in prediction of biofilm inhibition accurately, AMR marker identification using state of the art tools such as DeepARG, RGI and Deep neural frameworks to improve gene annotation from metagenomic and whole genome data, AI in surveillance and drug discovery tools like Alphafold2/3 and generative models to reduce discovery timelines.</p>

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Artificial intelligence for sustainable solutions in combating antimicrobial resistance through data driven health innovations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月4日

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  • Riya Bhattacharya,
  • Debajyoti Bose,
  • Kashif R. Siddique,
  • Raul Villamarin Rodriguez,
  • Aritra Ray

摘要

Artificial intelligence or AI is reshaping the fight against antimicrobial resistance (AMR) by offering innovative resources across multiple domains. In this work AI resource for microbial biofilms are covered which can identify compounds that disrupt resistant microbial communities by enhancing treatment efficacy. The AI resource for AMR markers, along with resources for surveillance and monitoring of AMR are also analysed. Accelerating the design of novel antibiotics by predicting bacterial protein structures is also presented with an emphasis on AI-powered diagnostics for AMR management. The potential of AI in AMR is extensive, with opportunities for real-time monitoring, generative drug development, and portable diagnostic tools, all contributing to a potent strategy by lowering expenses and responding to the evolution of resistance. Addressing existing limitations allows for the integration of human, animal, and environmental data through AI, offering a thorough, sustainable approach to combating AMR, which stands as one of the most significant challenges to global health. This study also explore digital tools and resources such as Machine Learning (ML) and Deep Learning (DL) in prediction of biofilm inhibition accurately, AMR marker identification using state of the art tools such as DeepARG, RGI and Deep neural frameworks to improve gene annotation from metagenomic and whole genome data, AI in surveillance and drug discovery tools like Alphafold2/3 and generative models to reduce discovery timelines.