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Molecular Docking with Diffusion Generative Models
Gabriele Corso · Hannes Stärk · Bowen Jing · Regina Barzilay · Tommi Jaakkola
Event URL: https://openreview.net/forum?id=fky3a3F80if »

Predicting the binding structure of a small molecule to a protein-a task known as molecular docking-is critical to drug design. Recent deep learning methods that frame docking as a regression problem have yet to offer substantial improvements over traditional search-based methods. We identify the drawbacks of a regression-based approach and instead view molecular docking as a generative modeling problem. We develop DockDiff, a novel diffusion process and generative model over the main degrees of freedom involved during docking. Empirically, DockDiff obtains a 37% top-1 success rate (RMSD <2A) on PDBBind, significantly outperforming the previous state-of-the-art of traditional docking (23%) and deep learning (20%) methods.

Author Information

Gabriele Corso (MIT)
Hannes Stärk (MIT)
Hannes Stärk

I am a first-year PhD student at MIT in the CS and AI Laboratory (CSAIL) co-advised by Tommi Jaakkola and Regina Barzilay. I work on geometric deep learning and physics-inspired ML and applications in molecular biology and other physical systems.

Bowen Jing (Massachusetts Institute of Technology)
Regina Barzilay (Massachusetts Institute of Technology)
Tommi Jaakkola (MIT)

Tommi Jaakkola is a professor of Electrical Engineering and Computer Science at MIT. He received an M.Sc. degree in theoretical physics from Helsinki University of Technology, and Ph.D. from MIT in computational neuroscience. Following a Sloan postdoctoral fellowship in computational molecular biology, he joined the MIT faculty in 1998. His research interests include statistical inference, graphical models, and large scale modern estimation problems with predominantly incomplete data.

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