User Webinar: Improving Pose Prediction Models for Drug Discovery

Join us for a webinar with David Errington and Cedric Bouysset from Recursion, the authors of a recent study that challenges the current benchmarks in protein–ligand pose prediction.
Tuesday, 9th September at 15:00 (BST)/ 16:00 (CEST)/ 10:00 (EDT)
While machine learning has revolutionized this field, many models still fall short in capturing the true biological relevance of protein–ligand interactions.
This webinar will explore how overlooking interaction can lead to inflated performance metrics and missed insights, especially in cofolding models. The authors will share their findings, discuss implications for drug discovery, and propose a more rigorous framework for evaluating pose prediction models.
Whether you’re a computational biologist, cheminformatician, or drug discovery professional, this webinar will present new perspectives on how to evaluate and improve pose prediction models more rigorously.
Who should attend:
- Drug Discovery Researchers;
- Data scientists;
- Professionals in computational biology, cheminformatics, and structural biology.
Speakers:
- David Errington, Senior AI Research Scientist, Recursion.
- Cedric Bouysset, Cheminformatics Research Scientist, Recursion.