Discngine Labs: Protein structure prediction - What’s next after AlphaFold?

The introduction of machine learning (ML) approaches for protein structure prediction has been a game changer in the life science industry. The breakthrough with DeepMind’s technology AlphaFold immediately impacted drug discovery research by providing access to thousands of unprecedented protein structures.  

However, the structure alone doesn’t tell the whole story. The scientific community is now building up on the foundation of this technology to introduce the functional context of AlphaFold predicted structures, accounting for non-protein molecules, interactions, and dynamics. 

In our fourth Discngine Labs, the experts from both pharma and academia will present how they use AlphaFold and other machine-learning-based technologies to develop methods that predict protein structures even more suitable for drug discovery applications.  

Hear from our speakers…

Event chair:

Seth Harris

Director of Computational Structural Biology

Genentech

Presenters:

Robbie Joosten

Research associate

Netherlands Cancer Institute

Christian Tyrchan

Team leader of Computational Chemistry

AstraZeneca

Jola Kopec

Senior Scientist I

Evotec

Andy Dore

Head of Structural Sciences

CharmTherapeutics

Sergey Bartunov

Heads of AI

Charm Therapeutics

… and discover:  

• What has happened since the release of AlphaFold, and where is its current biggest impact on drug discovery projects? 

• How can you adjust the AlphaFold algorithm toward drug discovery applications? 

• The limitations of ML-based protein structure predictions that persist and future perspectives