In the traditional paradigm, it can take up to ten years for a drug to come to market. For this episode, I am joined by guest Aaron Morris, Co-founder and CEO of PostEra, to talk about using AI to accelerate medicinal chemistry and bring cures to patients faster than ever before.
Aaron breaks down the medicinal chemistry process and explains how PostEra applies machine learning to drug discovery. The data landscape within drug discovery is particularly challenging and today, we learn about PostEra’s approach to gathering data, the data sets they build from, and how they find new uses for project-specific data. Hear about the importance of model interpretability and how to get a competitive advantage as an AI-powered startup.
Key Points:
Quotes:
“Though being reasonably competent on the machine learning side, I had a very, very steep learning curve when it came to getting up to speed with drug discovery chemistry and the applications of AI in that domain.” — Aaron Morris
“Drug discovery is going from biology to chemistry to medicine and PostEra squarely focuses, at least for now, on the chemistry angle. Our main focus is to build the world’s most advanced machine learning platform for what is referred to as medicinal chemistry.” — Aaron Morris
“PostEra is really the first AI company to pioneer machine learning across all three stages of how to design a molecule, how to make the molecule, and how to select the optimal set of molecules to test.” — Aaron Morris
“There is a lot of project-specific data that gets generated, and often what that means for PostEra is we’re having to be very inventive about how we try to get the most out of data even if it is not relevant.” — Aaron Morris
“If you want to build defensibility as a company, you have to have more than just innovations on model architecture.” — Aaron Morris
“Your typical drug today is taking anywhere between eight to ten years to come to market and obviously, we want to really accelerate that.” — Aaron Morris
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