Building on the trends in language processing, domain-specific foundation models are unlocking new possibilities. In the realm of drug discovery, Jason Rolfe is spearheading innovation at the intersection of AI and pharmaceuticals. As the Co-Founder and CTO of Variational AI, Jason leads a platform designed to generate novel small molecule structures that accelerate drug development. In this episode, he delves into how Variational AI uses foundation models to predict and optimize small molecules, overcoming the immense complexity of drug discovery by leveraging vast datasets and sophisticated computational techniques. He also addresses the key challenges of modeling molecular potency and why traditional machine-learning approaches often fall short. For anyone curious about AI's impact on healthcare, this conversation offers a fascinating look into cutting-edge innovations set to reshape the pharmaceutical industry. Tune in to find out how the types of breakthroughs we discuss in this episode could revolutionize drug development, bring new therapeutics to market across disease areas, and positively impact lives!

Key Points:

• An overview of Jason’s background and how it led him to create Variational AI.
• What Variational AI does for the small molecule domain for drug discovery.
• How they use foundation models to predict and enhance the design of small molecules.
• Defining small molecules, their appeal, and an overview of Variational AI's data sets.
• What goes into training Variational AI's foundation model.
• The computational infrastructure and algorithms necessary to process this data.
• Challenges of predicting molecular potency against disease-related protein targets.
• Various ways that Variational AI’s foundation model underpins everything they do.
• Evaluating progress: balancing predictive success with experimental validation.
• Lessons from developing foundation models that could apply to other data types.
• Jason’s funding and research-focused advice for leaders of AI-powered startups.
• The transformative impact of Variational AI’s technology on drug development.

Quotes:

“Rather than forming individual models for specific drug targets, we're creating a joint model over hundreds, eventually thousands of drug targets.” — Jason Rolfe

“Data quality is essential. In particular, if you're drawing from multiple different data sources, frequently, those sources aren't commensurable.” — Jason Rolfe

“If you don't have a proven track record where people are already throwing money at you, it is very challenging to try to bring a new technology from the drawing board into commercial application using venture funding.” — Jason Rolfe

“Whenever you're developing a new technology or product, you need to test early and often. Some of your intuitions will be good. Most of your intuitions will be a waste of time – The more quickly you can distinguish between those two classes, the more efficiently you can move toward success.” — Jason Rolfe

Links:

Variational AI

Variational AI Blog

Jason Rolfe on LinkedIn

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