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Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

AI and cellular images for universal drug discovery

Blake Borgeson (Recursion Pharmaceuticals), Nan Li (Obvious Ventures)
11:55am–12:35pm Tuesday, September 19, 2017
Verticals and applications
Location: Imperial B Level: Beginner
Secondary topics:  Biopharmaceuticals, Deep learning
Average rating: **...
(2.00, 1 rating)

What you'll learn

  • Understand the progression from traditional data science approach to an AI- and ML-driven approach
  • Explore an instructive and inspiring example of generating proprietary dataset for a vertical AI application


AI has already become a silent force behind the scenes in many of our interactions with the digital world . Catalyzed by the success of deep learning and increasingly accessible open source frameworks for data science and machine learning (e.g., TensorFlow, scikit-learn, and Keras), AI is on an unprecedented path toward democratization. However, democratization is also the first step toward commoditization. Standout AI companies will increasingly require access to proprietary datasets to build specialized models in vertical use cases.

Recursion Pharmaceuticals applies computer vision and machine learning to create a high-dimensional feature space in which to evaluate cellular health broadly across hundreds of disease states. Blake Borgeson and Nan Li offer a technical overview of how Recursion leverages cellular phenotyping for drug discovery. Blake and Nan first outline a brief history of the development and emergence of cellular morphological profiling and describe the principles supporting the ability to turn a biological question of interest into a data science problem. They then explore the deep learning tools and architectures Recursion uses to level up the effectiveness and flexibility of imaging-based approaches to biology, paying special attention to the power of combining convolutional neural networks with the company’s growing mass of rich proprietary cellular imaging data and to efforts that may in the future enable a transition away from brute force search toward predictive drug discovery. Blake and Nan conclude with a summary of early medical discoveries enabled by the platform and what this means for the future of healthcare.

Photo of Blake Borgeson

Blake Borgeson

Recursion Pharmaceuticals

Blake Borgeson is the cofounder and CTO of Recursion Pharmaceuticals, where he is leading the computational development of a drug discovery platform combining high-throughput experiments and machine learning that is capable of finding potential treatments for hundreds of diseases rapidly and in parallel. Previously, he researched and built real-time navigation software for surgical procedures at the M.E. Müller Institute in Bern, Switzerland and cofounded, an ecommerce company that currently employs over 350 in Austin, Texas. Blake holds a PhD in bioinformatics from UT Austin’s Marcotte Lab, where his research used machine learning to exploit new experimental techniques in rapidly mapping protein complexes, and a BS in electrical engineering from Rice University.

Photo of Nan Li

Nan Li

Obvious Ventures

Nan Li brings a mixed technology, investing and entrepreneurial background to Obvious Ventures. He is also an adjunct lecturer at Stanford on venture capital. Nan has been a venture investor and advisor for the past five years, working with companies applying technology toward solving big problems. Previously, he managed early-stage tech investments for Eric Schmidt’s Innovation Endeavors; led product, operations, and finance at Gigwalk, a mobile, crowdsourced data and analytics company funded by Greylock Partners and August Capital; was a VC at Bain Capital Ventures; was a management consultant at Bain & Company; and served as a PM at Microsoft. Nan holds a BSE in computer science engineering from the University of Michigan. He grew up in Detroit after emigrating from China. He enjoys music, photography, culture, puzzles, all Detroit sports, and general nerdom.