{"slug":"phagos-uses-generative-ai-on-aws-to-match-bacteriophages-to-bacterial-infections","url":"https://findausecase.com/use-cases/phagos-uses-generative-ai-on-aws-to-match-bacteriophages-to-bacterial-infections","title":"Phagos uses generative AI on AWS to match bacteriophages to bacterial infections","description":"Phagos, a Paris-based biotech startup, uses generative AI models built with Amazon SageMaker AI to match bacteriophages to target bacteria for phage therapy, replacing a manual trial-and-error process. The AI models cut wet-lab testing needs by 50% and reduced phage-candidate screening time by 99.5%, from 29 hours to 10 minutes per bacteria. Phagos has treated more than half a million animals in France and can now develop a new treatment in two months versus 10+ years for traditional antibiotic development.","company":"Phagos","industry":"Life Sciences","country":"France","aiCapabilities":["Generative AI","Predictive Analytics","Digital Twins & Simulation"],"technology":["Amazon SageMaker AI","Amazon EC2","Amazon S3","Amazon RDS"],"deployment":"Unknown","problemStatement":"Antibiotic resistance is a growing global threat, with bacterial infection expected to become the leading cause of mortality by 2050, and traditional antibiotic development can take more than 10 years and cost billions of dollars. Matching the right bacteriophage to a target bacterium had been a largely manual trial-and-error process, an exponential problem unable to be solved manually given the astronomically large number of possible phage-bacteria combinations.","solutionApproach":"Phagos uses Amazon SageMaker AI to train and fine-tune generative AI models on genomic data, from public databases and its own lab-generated data, that simulate millions of phage-bacteria interactions and determine the optimal phage characteristics for killing a given bacteria strain, powering its proprietary AI platform, Alphagos. Phagos also relies on high-performance GPU instances on Amazon EC2 for the computational power needed to run biological simulations, Amazon S3 as its central data lake, and Amazon RDS to manage and query structured data, working closely with AWS and AWS consulting partners.","businessValue":"The AI predictions reduce wet-lab testing needs by 50% and deliver a 99.5% time savings when screening phage candidates, down from 29 hours to 10 minutes per bacteria. Phagos can now develop a new treatment in two months from scratch versus 10+ years for traditional antibiotic development. Phagos has treated more than half a million animals in France after demonstrating efficacy over more than 10 clinical trials, and received approval from the French Agency for Veterinary Medicinal Products to market its solution in France as personalized veterinary medicines. In an early field test at a French oyster farm, applying an identified phage reduced oyster mortality by 40%.","evidence":{"band":"high"},"sourceUrl":"https://aws.amazon.com/solutions/case-studies/phagos-case-study","dates":{"publishedAt":"2026-08-16T08:25:40.835Z","publishedAtSource":"ledger","updatedAt":"2026-08-18T10:29:44.208Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/phagos-uses-generative-ai-on-aws-to-match-bacteriophages-to-bacterial-infections. Bulk republication requires permission."}