Connor W. Coley

Connor W. Coley is an Assistant Professor at MIT in the Department of Chemical Engineering with a shared appointment in the Department of Electrical Engineering and Computer Science. His work in computer assistance and automation for organic synthesis has included the development of a data-driven synthesis planning program and in silico strategies for predicting the outcomes of organic reactions. His continuing research interests are in how data science, statistical learning, and laboratory automation can be used to streamline discovery in the chemical sciences. Connor has been named one of C&EN’s “Talented Twelve” and one of Forbes Magazine’s “30 Under 30” for Healthcare. 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.

Connor's group develops new types of computer-aided molecular design and synthesis for the chemical sciences to accelerate the research enterprise. They aim to do so through:

1. the development of robust domain-tailored machine learning models for predicting molecular function and generating new structures; and
2. the use and/or generation of chemical data sets to learn generalizable patterns of organic reactivity, designed to support and interface; with
3. on-demand synthesis and testing for closed-loop validation of new chemical entities.