Trillions of microbes, including bacteria, fungi and viruses, inhabit the human gut. Increasingly, research is illuminating the importance of gut microbes for overall health. Many diseases have been linked to imbalances in the composition and functions of gut microorganisms, including colon cancer, obesity, Parkinson’s disease, autism, and depression. Evidence is showing how individuals’ diets, lifestyles, and medications shape gut microbiome health. In fact, many antimicrobial and non-antimicrobial drugs are suspected of altering the structure and behaviour of intestinal microbes. In a recent study, we apply machine learning to predict adverse drug effects on gut bacterial growth. Leveraging a dataset of over 18,600 bacteria-drug interactions, a tuned extra trees algorithm was developed to predict anti-gut bacterial effects of any drug. The final tuned, ensemble model achieved performance metrics that surpass existing tools and human ability to guess. The algorithm provides a fast and efficient way to predict drug effects on gut bacterial growth. This can lead to better understanding of drug-microbiome interactions and streamline in vitro and in vivo experimentation in drug development.