How Machine Learning is Transforming Drug Discovery
Daphne Koller, a veteran of AI, explains why she left academia for a chance to change the pharmaceutical industry.
In a world where a drug takes years and billions of dollars to develop, just one in 20 candidates makes it to market. Daphne Koller is betting artificial intelligence can change that dynamic.
Twenty years ago, when she first started using artificial intelligence to venture into medicine and biology, Koller was stymied by a lack of data. There wasn’t enough of it and what there was, was often not well suited to the problems she wanted to solve. Fast-forward 20 years, however, and both the quantity and quality of data, and the tools for studying biology, have advanced so dramatically that the adjunct professor of computer science at Stanford founded a company, insitro, that uses machine learning (a subspecialty of artificial intelligence) to explore the causes and potential treatments for some very serious diseases.
She tells bioengineer and Stanford Institute for Human-Centered AI associate director Russ Altman about the lessons she’s learned along the way, and the challenges and rewards of getting diverse teams of experts from many fields to speak the same language. It’s all on this episode of Stanford Engineering’s The Future of Everything podcast. Watch here, and subscribe to the podcast here.
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