"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 | 15:15 › 16:15 |
| 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
Organizer
- Kevin Maik Jablonka, Puck van Gerwen, Philippe Schwaller, Andres Bran, Jeff Guo