Automated Materials Synthesis Keynote

Abstract: The ability to quickly iterate through design cycles relies on our ability to synthesize molecules and materials found in virtual libraries or proposed by generative models. In this talk, we will focus on a primary consideration of molecular design workflows: the chemical space that comprises the search space for a molecular screening/optimization campaign. That is, the manner in which the search is constrained to a finite library of molecules or, following an increasingly popular trend, the manner in which the search navigates a virtually infinite space of molecules. We will talk about how models to predict chemical reactivity inform our ability to define and navigate these spaces. Further, we will discuss the challenge of sample efficiency, as most algorithms for molecular design operate in a regime that is severely misaligned with the reality of experimental work in terms of the number of candidates that can be evaluated within a practical budget. Bio: Connor W. Coley is an Assistant Professor at MIT in the Department of Chemical Engineering and the Department of Electrical Engineering and Computer Science. He received his B.S. and Ph.D. in Chemical Engineering from Caltech and MIT, respectively, and did his postdoctoral training at the Broad Institute. His research group at MIT develops new methods at the intersection of data science, chemistry, and laboratory automation to streamline discovery in the chemical sciences with an emphasis on therapeutic discovery. Key research areas in the group include the design of new neural models for representation learning on molecules, data-driven synthesis planning, in silico strategies for predicting the outcomes of organic reactions, model-guided Bayesian optimization, and de novo molecular generation. Connor is a recipient of C&EN’s “Talented Twelve” award, Forbes Magazine’s “30 Under 30” for Healthcare, the NSF CAREER award, and the Bayer Early Excellence in Science Award. Outside of MIT, Connor serves as an advisor to both early- and late-stage companies including Entos, Revela, Galixir, Kebotix, Anagenex, and Dow.