University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Geometric Deep Learning for Structure-Based Drug Design

Geometric Deep Learning for Structure-Based Drug Design

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact .

Teams link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.

Geometric deep learning is revolutionizing structure-based drug design (SBDD), enabling us to harness the full potential of three-dimensional protein structures for drug development. In this talk, I will present a comprehensive overview of how geometric deep learning approaches advance critical tasks in SBDD , from binding site prediction to linker design. I will examine the latest architectures that can effectively process and learn from 3D structural data and discuss their practical applications in drug discovery pipelines. Looking ahead, I will also highlight some emerging opportunities in this rapidly evolving field.

This talk is part of the Machine Learning Reading Group @ CUED series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity