Bacteria in the human intestine (the gut microbiota) are capable of biotransforming some of the drugs they are exposed to. Gut bacteria derive this biotransformation capability from their innate arsenal of enzymes, which allows them to live, thrive and survive in their environment. Several studies provided evidences of microbiome-mediated drug biotransformation, however extensive laboratory testing is required to produce such results, limiting their scalability. This project aims at predicting, prioritizing and eventually upscaling the detection of gut bacteria biotransformation of drugs in-silico by combining untargeted metabolomics and machine learning to analyze bacterial cultures grown in different chemical environments.