Event
AI Seminar: Explaining molecular properties with natural language
Location
Date
Type
This will be a hybrid event. You can show up physically in HCØ, Aud 8 or join on Zoom: https://ucph-ku.zoom.us/j/65495965794?pwd=ZTNEaVplRXlwUHd3MmtRb3dublNJZz09
Title
Explaining molecular properties with natural language
Abstract
Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of interpretability in “black-box” models. Explainable artificial intelligence (XAI) is a branch of AI which addresses this drawback by providing tools to interpret DL models and their predictions. Molecular property prediction is an unusual space of XAI because the input features are graphs – which have poorly defined gradients. I will review the principles of XAI in the domain of chemistry and emerging methods for creating and evaluating explanations. Then describe its application to explaining solubility, blood-brain barrier permeability, and the scent of molecules. We show that XAI methods like counterfactuals and descriptor attributions can both explain DL predictions and give insight into structure-property relationships. Finally, we discuss how a two-step process of highly accurate black-box modeling and then creating explanations gives both highly accurate predictions and clear structure-property relationships.
Bio
Andrew White graduated from Rose-Hulman Institute of Technology in 2008 with a BS in chemical engineering. While at Rose, he spent a year studying at the Otto-von Guericke Universität and the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. Dr. White completed a PhD in chemical engineering at the University of Washington in 2013. The thesis topic was the creation of non-fouling biomimetic surfaces with computational modeling. Dr. White worked with Professor Greg Voth at University of Chicago as a Post-doctoral fellow in the Institute for Biophysical Dynamics from 2013-2014. In Chicago, he developed new methods for combining simulations and experiments. Dr. White joined the University of Rochester in Chemical Engineering in 2015 and is currently an associate professor. He has joint appointments in the Chemistry Department, Biophysics, Materials Science, and Data Science programs. Dr. White received a National Science Foundation CAREER award in 2018 and an Outstanding Young Investigator Award from the National Institutes of Health in 2020. He joined Vial Health Technologies as VP of AI in 2023. Dr. White has authored a textbook on deep learning for molecules and materials, which is freely available at https://dmol.pub.
Email: andrew.white@rochester.edu
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