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Learning from Life: Lessons from ML and Biophysical Simulations

Re Mansbach, Concordia University
Tuesday January 7, 12-1pm
Zoom Link:听
In Person: 550 Sherbrooke, Room 189

Abstract:听Proteins and peptides are the molecular motors that keep our cells running. Many of them can be used as drug targets or biomolecular drugs for therapeutic applications, but the search space of the possible amino acid combinations forming even a short peptide is astronomical. To design new peptides for specific applications, we need a larger-scale understanding of the entire search space. In my lab, we address these questions through a combination of interpretable machine learning and optimization techniques to rationally explore ML-associated search spaces for peptides and the parameterization and application of simulations to understand candidates of particular interest at a detailed molecular level. In this presentation, I will introduce the tools of our trade and discuss how we apply them to some of our ongoing projects, with a particular focus on antimicrobial peptide understanding and design.