Tackling antimicrobial resistance through innovative combination therapies, artificial intelligence, and next-generation biological strategies.
Harnessing the synergistic power of phytochemicals and conventional antibiotics to combat WHO-priority pathogens. We screen plant-derived bioactive compounds for their ability to potentiate antibiotic efficacy, reverse resistance mechanisms, and reduce minimum inhibitory concentrations against critical drug-resistant bacteria.
Leveraging artificial intelligence and machine learning to accelerate the discovery and repurposing of drugs against WHO-priority pathogens. Our computational pipelines integrate molecular docking, deep learning, and network pharmacology to identify novel therapeutic candidates from existing drug libraries.
Investigating the co-selection pressure of heavy metals on the emergence and propagation of antibiotic resistance in environmental and clinical bacterial isolates. We explore how metal contamination in soil and water ecosystems drives cross-resistance and co-resistance through shared genetic determinants.
Developing AI-powered image analysis pipelines that predict antimicrobial resistance directly from bacterial morphology. By training deep learning models on microscopy images, we aim to enable rapid, culture-free resistance profiling that can dramatically shorten diagnostic turnaround times in clinical settings.
Exploring bacteriophages as a targeted biological weapon against multi-drug-resistant priority pathogens. We isolate, characterize, and engineer lytic phages with high specificity to combat infections where conventional antibiotics have failed, offering a precision alternative for the post-antibiotic era.