Deep learning analysis of imaging and metabolomic data to accelerate antibiotic discovery against antimicrobial resistance

Deep learning analysis of imaging and metabolomic data to accelerate antibiotic discovery against antimicrobial resistance is sponsored by Horizon Europe. This opportunity supports mission-aligned projects and measurable outcomes.

Deep learning analysis of imaging and metabolomic data to accelerate antibiotic discovery against antimicrobial resistance Smart tech tackles superbugs Antimicrobial resistance (AMR) poses a severe threat to global health, with few new antibiotics on the horizon. Current drug discovery methods are slow, often missing key details about how new antibiotics work.

In this context, the ERC-funded AI4AMR project aims to tackle this challenge by integrating microbiology, genetics, AI, and advanced screening techniques to create a faster, more efficient pipeline for discovering new antibiotics. By leveraging deep learning to analyse vast datasets of bacterial responses, AI4AMR can pinpoint drug targets and mechanisms of action in record time.

This innovative approach promises to uncover novel antibiotics, including from complex natural products, ultimately accelerating the fight against AMR and ensuring more effective treatments for the future. Show the project objective Hide the project objective Antimicrobial resistance (AMR) is one of the most pressing global health problems of our times.

To counteract AMR, we urgently need new antibiotics, particularly with novel modes of action (MoA). However, while typical antibiotic screening pipelines can identify compounds that impair bacterial growth, they are unable to predict drug targets and MoA so must be followed up by time-consuming target identification steps.

By synergizing our expertise in microbiology, genetics, advanced microscopy, metabolomics, medicinal chemistry, computational biology and artificial intelligence (AI), we propose to create a new pipeline at the forefront of the antibiotic discovery field that will be capable of informing simultaneously on the bioactivity and MoA of new antibiotic candidates.

Working with seven pathogens, our improved acquisition strategies for both imaging-based high-content screening and metabolomics will generate a massive dataset of rich multidimensional phenotypes of libraries of genetic mutants and of bacteria exposed to a range of perturbants, at unprecedented scale.

Deep learning analyses will then enable us to explore these massive datasets to correlate chemical-induced phenotypes to those from mutants, linking drugs to genes to elucidate the target/MoA of new drugs. This innovative pipeline will enable us to explore unique chemical spaces, including complex natural product extracts (without the need for isolation of individual components) and novel synthetic compounds.

Promising candidates with novel MoA will be tested against drug-resistant clinical isolates and against a future pandemic 'pathogen X', demonstrating our pipeline as an AI-powered solution for achieving higher productivity in antibiotic discovery.

AI4AMR will provide the community with a new pipeline to efficiently screen large compound libraries to identify novel antibiotics and define their MoA and target, helping directly to combat AMR. Fields of science (EuroSciVoc) CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary .

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