Sign up to save your podcasts
Or
Share 17. Noah Weber - Celeris Therapeutics: MLOps and Degrader Molecule Design
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
17. Noah Weber - Celeris Therapeutics: MLOps and Degrader Molecule Design
# Intro
Noah Weber is the Chief Technology offer at Celeris Therapeutics. Celeris is discovering candidates for targeted protein degradation in silico using Artificial Intelligence, to accelerate drug discovery and find cures for diseases like Alzheimer's.
In the first part of the show, he shares his experience on the importance of good MLOps practices and the importance of cloud solutions for the development of AI products at scale.
In the second part, we talk about the core technology that Celeris has developed to solve the problem of finding candidates for targeted protein degradation. Noah explains the problem they are solving from a computational perspective and we discuss how they use Bayesian optimization to evaluate and rank candidates in silico, reducing the costs and time for drug discovery.
# References
Noah Weber - https://www.linkedin.com/in/noahweber123
Celeris Therapeutics - https://celeristx.com
AI-driven degrader design - https://www.youtube.com/watch?v=p2OjZ5afyLw
ACIT Podcast with Noah Weber - https://open.spotify.com/episode/61eXYwKpVuj1w8l0fiyEh5?si=n0vKpaXLTNKz0nwIpJaGMw
View all episodes
By Manuel Pasieka# Intro
Noah Weber is the Chief Technology offer at Celeris Therapeutics. Celeris is discovering candidates for targeted protein degradation in silico using Artificial Intelligence, to accelerate drug discovery and find cures for diseases like Alzheimer's.
In the first part of the show, he shares his experience on the importance of good MLOps practices and the importance of cloud solutions for the development of AI products at scale.
In the second part, we talk about the core technology that Celeris has developed to solve the problem of finding candidates for targeted protein degradation. Noah explains the problem they are solving from a computational perspective and we discuss how they use Bayesian optimization to evaluate and rank candidates in silico, reducing the costs and time for drug discovery.
# References
Noah Weber - https://www.linkedin.com/in/noahweber123
Celeris Therapeutics - https://celeristx.com
AI-driven degrader design - https://www.youtube.com/watch?v=p2OjZ5afyLw
ACIT Podcast with Noah Weber - https://open.spotify.com/episode/61eXYwKpVuj1w8l0fiyEh5?si=n0vKpaXLTNKz0nwIpJaGMw
More shows like Austrian Artificial Intelligence Podcast
View all
Inside Austria
17 Listeners