Machine Learning for Antibiotic Discovery
To address the antibiotic-resistance crisis, we trained a deep neural network to predict new antibiotics. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub – halicin – that is structurally divergent from conventional antibiotics and displays activity against a wide spectrum of pathogens. Halicin also effectively treated Clostridioides difficile and Acinetobacter baumannii infections in mice. Deep learning approaches have utility in expanding our antibiotic arsenal.
3 Key Takeaways:
*We leveraged a message passing neural network to predict structurally novel antibiotics
*We discovered halicin, which displayed bactericidal efficacy against a broad spectrum of bacterial pathogens
*Machine learning allows us to explore vast chemical spaces in the search for new medicines
Jonathan Stokes is a Banting Fellow in the laboratory of James Collins at the Broad Institute of MIT and Harvard. He received his BHSc in 2011, graduating summa cum laude, and his PhD in antimicrobial chemical biology in 2016, both from McMaster University. His research applies a combination of chemical biology, systems biology, and machine learning approaches to develop novel antibacterial therapies with expanded capabilities over conventional antibiotics. Dr. Stokes is the recipient of numerous awards, including the Canadian Institutes of Health Research Master’s Award, the Colin James Lyne Lock Doctoral Award, and was ranked first of just 23 postdoctoral scholars to be awarded the prestigious Banting Fellowship.