ACS Presidential Symposium on Machine Learning

The Dreyfus Foundation has organized an American Chemical Society (ACS) Presidential Symposium on Machine Learning that will be held at the fall national meeting of the ACS in San Francisco on Tuesday, August 15, 2023. The symposium is the capstone to the Foundation’s Program in Machine Learning in the Chemical Sciences and Engineering that concluded in July 2022. The event is open to all attendees of the fall ACS meeting.

The distinguished speakers are Klavs Jensen (MIT), Yu-Shan Lin (Tufts University), Milad Abolhasani (North Carolina State University), Brett Savoie (Purdue University), Andrew Ferguson (University of Chicago), Thomas Miller (Entos, Inc.).

The anticipated agenda is provided below. A reception will follow. For up-to-date information including how to attend, please visit the ACS meeting website.

Dreyfus Foundation ACS Presidential Symposium on Machine Learning
Room 24/25, South Building (Moscone Center)

Note: All times shown are Pacific.

10:00 am: Morning Welcome

  • Scott Walter, President, The Camille and Henry Dreyfus Foundation
  • Milan Mrksich, Symposium Chairman; Board Member, The Camille and Henry Dreyfus Foundation; and Vice President for Research and Henry Wade Rogers Professor of Biomedical Engineering, Northwestern University
  • Richard Zare, Session Moderator; Board Member Emeritus, The Camille and Henry Dreyfus Foundation; and Marguerite Blake Wilbur Professor of Natural Science, Stanford University

10:20 am: Klavs Jensen, Chemical Engineering, MIT
Accelerating Chemical Discovery and Development with Machine Learning and Automation

10:55 am: Yu-Shan Lin, Chemistry, Tufts University
Overcoming the Structural Challenges of Cyclic Peptide Design with Machine Learning

11:30 am: Milad Abolhasani, Chemical and Biomolecular Engineering, North Carolina State University
Accelerated Materials Discovery and Optimization Enabled by an Autonomous Fluidic Lab

2:00 pm: Afternoon Welcome

  • Milan Mrksich
  • Richard Zare

2:05 pm: Brett Savoie, Chemical Engineering, Purdue University
General Machine Learning Strategies for Solving Chemical Deduction Problems

 2:40 pm: Andrew Ferguson, Pritzker School of Molecular Engineering, University of Chicago
Machine Learning-Guided Directed Evolution of Functional Proteins

3:05 pm: Thomas Miller, Entos, Inc.
How Generative AI Solved the 1000-Fold Selectivity Grand Challenge in HER2 Cancer Drug Discovery

3:40 pm: Closing Remarks

  • Milan Mrksich

Reception to follow.