Modeling Molecular Interactions with Machine Learning

Hannes Stärk, MIT
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We first provide a high-level overview of different directions in modeling molecular interactions where deep learning methods have already had significant positive real-world impact. Then we focus on the particular task of predicting the structure in which a small molecule binds to a protein as a case study by investigating how we address it with our DiffDock method. DiffDock is a diffusion generative model for predicting binding structures -- a fundamental task in structural biology -- which we will discuss in greater detail.

Speaker Bio: Hannes Stärk is a first-year PhD student at MIT in the CS and AI Laboratory (CSAIL) co-advised by Tommi Jaakkola and Regina Barzilay. He works on geometric deep learning and physics-inspired ML and applications in molecular biology and other physical systems.

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