Date(s) - 10/03/2023
2:30 pm - 3:30 pm
Prof. Jon Stokes
Dept. of Biochemistry & Biomedical Sciences, McMaster University
Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. In this talk, we will explore the utility of discriminative and generative machine learning methods for novel antibiotic discovery against this burdensome pathogen.
Jon received his BHSc in 2011 and his PhD in antimicrobial chemical biology in 2016, both from McMaster University. From 2017 to 2021 he was a postdoctoral fellow at the Broad Institute of MIT and Harvard, carrying a prestigious Banting Fellowship from 2018-2020. Upon completing his postdoc, Jon established his laboratory back at McMaster in the Department of Biochemistry and Biomedical Sciences in August 2021. The Stokes lab leverages a mindful balance of experimental and computational approaches to discover the next generation of life-saving antibiotics with novel structures and functions that expand the capabilities of these medicines beyond the current state of the art. One of our primary interests, quite broadly, is in the application of deep learning approaches to help us predict the antibacterial properties of structurally novel small molecules. Moreover, we seek to elucidate the molecular mechanisms underlying tolerance to antibiotics, which is the case where conventional bactericidal antibiotics fail to eradicate genetically antibiotic-susceptible bacterial cells. Indeed, these poorly understood antibiotic tolerant bacterial populations are responsible for prolonging antibiotic treatment durations in immunocompromised patients and facilitating the evolution of bona fide antibiotic resistance.
In-Person: ABB 102