"AI in chemistry and beyond: ML for modeling molecular interactions: DiffDock as example for docking prediction" seminar by Hannes Stärk


Event details

Date 18.04.2023
Hour 15:1516:15
Speaker Hannes Stärk is a PhD student at MIT in the CS and AI Laboratory (CSAIL) co-advised by Tommi Jaakkola and Regina Barzilay.
Location Online
Category Conferences - Seminars
Event Language English

We will discuss the use of machine learning methods for modeling molecular interactions with a focus on protein-ligand docking. Predicting the binding structure of a small molecule ligand to a protein -- a task known as molecular docking -- is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial improvements in accuracy. We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses. To do so, we map this manifold to the product space of the degrees of freedom (translational, rotational, and torsional) involved in docking and develop an efficient diffusion process on this space.

Practical information

  • Informed public
  • Free


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