David healey

recursion pharmaceuticals

Are you building an AI-first technology company in pharma, materials or biology? Head over to Air Street Capital.

The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. In the lead up to our 5th annual event on June 28th 2019 in London, we’re running a series of speaker profiles to shed more light on what you can expect to learn on the day!

One of our focus areas at RAAIS 2019 is on machine learning methods in the life sciences. To that end, we’re excited to welcome David Healey, Senior Data Scientist at Recursion Pharmaceuticals. The company is recognised as a leader in taking advantage of recent advances in automation, computer vision, and AI to enable faster drug discovery at scale. The core discovery platform uses robotics and automation to gather a large amount of rich data on the way that cells respond to drugs in the context of a broad range of diseases. Recursion then uses advanced analytics and machine learning to identify patterns in the biological response and search for potential cures, testing thousands of compounds on hundreds of diseases in parallel. In a short time, Recursion has been able to build a relatively large preclinical pipeline of treatments in the areas of rare genetic diseases, inflammation, and infectious disease, and has recently begun clinical trials on drug candidates for two rare genetic diseases.

recursion bio_lab.jpg

As one of the first data scientists at Recursion, David has been centrally involved building Recursion’s core machine learning, data platform, and data science team from the ground up. He joined Recursion in 2016 after completing a Ph.D. in Biology at MIT’s Center for the Physics of Living Systems.

In his talk at RAAIS 2019, David will introduce Recursion’s approach to mapping human cellular biology and tease out the complex interactions between diseases and drug treatments. He will describe recent results in using machine learning to predict the biology and pharmacology of chemical compounds from microscopy images and chemical structure and discuss his outlook on the future of AI-enabled drug discovery.

Welcome #RAAIS2019, David!