The Legacy of Stanford’s Biomedical Informatics Program

<p>Harry traveled to the San Francisco Bay Area this summer, and while there he interviewed the co-founders of three local data-driven diagnostics and drug discovery startups, all of whom participated in the same graduate program: the Biomedical Informatics Program at Stanford's School of Medicine.  Joining Harry were Aria Pharmaceuticals co-founder and CEO Andrew Radin, BigHat Biosciences co-founder and chief scientific officer Peyton Greenside, and Inflammatix co-founder and CEO Tim Sweeney. The conversation covered how each company's work to advance healthcare and therapeutics rests on data and  computation, and how the ideas, skills, connections each entrepreneur picked up at Stanford have played into their startups and their careers.</p><p>Radin's company, formerly known as twoXar, models pathogenesis computationally to identify potential drug molecules, shaving years off the drug development process. Radin developed Aria’s core technology, a collection of proprietary algorithms for discovering novel small molecule therapies. The algorithms incorporate system biology data, disease-specific data, chemistry libraries, and more than 60 separate AI methods to sift through molecules with known chemistry to find those with novel mechanisms of action and favorable safety profiles absorption properties. Whereas traditional drug discovery methods have a 1-2% success rate after 4 years, Aria claims its approach has a 30% success rate after just 6 months. It has a pipeline of at 18 drug candidates in areas including kidney, lung, and liver diseases, lupus, cancers of the liver and lung, glioblastoma, and glaucoma. Radin holds MS and BS degrees in computer science from Rochester Institute of Technology, studied computational biology and medicine through the Stanford Center for Professional Development, and was formerly an advisor to several venture capital firms and startup accelerators. </p><p>Greenside started BigHat to combine wet-lab science and machine learning with the goal of speeding up the design of antibody th