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Stanford Biotechnology Symposium 2020

Event Details:

Tuesday, October 27, 2020

About our annual biotechnology symposium

Each year, the Stanford-NIH Biotechnology Predoctoral Training Grant program hosts a symposium, for the purpose of highlighting exciting cutting-edge research at Stanford, and facilitating connections with the biotechnology industry. 

Our 2020 symposium was held virtually, as a 2-hour mini symposium, and our agenda featured keynote speakers Dr. Daphne Koller and Dr. Peter S. Kim, and research talks by several of our program trainees.  

Dr. Daphne Koller -- "Machine Learning: A New Approach to Drug Discovery"    



Daphne Koller is CEO and Founder of insitro, a company applying machine learning to drug development. Daphne was a Computer Science Professor at Stanford, co-founder and co-CEO of Coursera, and Chief Computing Officer of Calico. Daphne was one of TIME Magazine’s 100 most influential people and is a MacArthur Fellow, a member of the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences and the International Society of Computational Biology.

Machine Learning: A New Approach to Drug Discovery. Modern medicine has given us effective tools to treat some of the most significant and burdensome diseases. At the same time, it is becoming consistently more challenging to develop new therapeutics: clinical trial success rates hover around the mid-single-digit range; the pre-tax R&D cost to develop a new drug (once failures are incorporated) is estimated to be greater than $2.5B; and the rate of return on drug development investment has been decreasing linearly year by year, and some analyses estimate that it will hit 0% before 2020. A key contributor to this trend is that the drug development process involves multiple steps, each of which involves a complex and protracted experiment that often fails. We believe that, for many of these phases, it is possible to develop machine learning models to help predict the outcome of these experiments, and that those models, while inevitably imperfect, can outperform predictions based on traditional heuristics. The key will be to train powerful ML techniques on sufficient amounts of high-quality, relevant data. To achieve this goal, we are bringing together cutting edge methods in functional genomics and lab automation to build a bio-data factory that can produce relevant biological data at scale, allowing us to create large, high-quality datasets that enable the development of novel ML models. Our first goal is to engineer in vitro models of human disease that, via the use of appropriate ML models, are able to provide good predictions regarding the effect of interventions on human clinical phenotypes. Our ultimate goal is to develop a new approach to drug development that uses high-quality data and ML models to design novel, safe, and effective therapies that help more people, faster, and at a lower cost.

Dr Koller's Presentation Slides:  insitro_talk_2020_10_27.pdf

Dr. Peter S. Kim - "Preventing Diseases with Vaccines"

Peter S. Kim is the Virginia & D.K. Ludwig Professor of Biochemistry at Stanford University School of Medicine and an Institute Scholar of Stanford ChEM-H.  He is also the Lead Investigator of the Infectious Disease Initiative at the Chan Zuckerberg Biohub.  He was President of Merck Research Laboratories from 2003–2013 and oversaw development of more than 20 new medicines and vaccines, including JANUVIA, GARDASIL, ISENTRESS, ZOSTAVAX, and KEYTRUDA. Earlier, he was Professor of Biology at MIT, Member of the Whitehead Institute and an HHMI Investigator.  He is known for discovering a salient component of how proteins cause viral membranes to fuse with cells and has pioneered efforts to create an AIDS vaccine based on inhibiting the HIV-1 membrane-fusion process. He is a member of the National Academy of Sciences, the National Academy of Medicine and the National Academy of Engineering.

Program Trainee research talks

  • Katie Antilla: "A Magnetoresistive Biosensor Assay for Detecting Circulating Tumor DNA in Non-Small Cell Lung Cancer"
  • Eva González Díaz: "Mimicking breast cancer bone metastases using spatially patterned microribbon hydrogels"
  • Dana Cortade: "Giant magnetoresistive nanosensors: a precision medicine platform for pain"

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