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AIC Seminar Series

Artificial Intelligence Methods for Molecular Property Prediction

Evan FeinbergStanford University[Home Page]

Notice:  Hosted by Greg Kovacs

Date:  Thursday, June 14th 2018 at 4:00pm

Location:  EK255 (SRI E building)  (Directions)

Join Remotely: 

VTC available (contact braz@ai.sri.com for info).
   Abstract

It costs $1.2 billion to develop a single FDA-approved drug. That price tag is rapidly rising. Can Artificial Intelligence (AI) live up to the hype it has rightly garnered in other fields and reverse the seemingly inexorable trend of "Eroom's law" in developing new medicines? Vijay Pande and colleagues and I recently published two papers that tell two distinct stories about AI drug discovery.

I wrote an "Spatial Graph Convolutions for Drug Discovery" describes new deep neural network architectures for modeling drug-receptor interactions. We argue that the future of predicting the interactions between a drug and its prospective target demands more than simply applying deep learning algorithms from other domains, like vision and natural language, to molecules.

2. "Machine Learning Harnesses Molecular Dynamics to Discover New μ Opioid Chemotypes" describes an algorithm that leverages protein motion to enrich the search for active molecules. We then applied the method to find a new chemical scaffold that we experimentally verified is an agonist for the µ Opioid Receptor.

   Bio for Evan Feinberg

I suppose I have come closer than most to treating one’s own health condition. For my Ph.D. in Computational Biophysics at Stanford, I have developed Artificial Intelligence methods for drug discovery. These methods range from new deep neural network architectures to analysis techniques for molecular dynamics simulations. I have applied these tools to discover a completely novel opioid agonist scaffold, which we hope will improve the treatment of chronic pain. Most recently, in a collaboration with Merck, we showed that our methods significantly improve the prediction of key molecular properties relevant to pharmaceutical development. I was diagnosed with a pain-inducing, genetic myopathy while in college at Yale, where I studied physics. In graduate school, I developed the tools necessary to make better drugs to tackle such conditions. After graduating this June, I’m figuring out how best to translate that knowledge to help people in the real world!

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