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