Connor Coley

The prototypical discovery workflow for molecules and materials involves iterating through design-build-test loops. One paradigm of chemical space exploration is virtual screening, where pre-enumerated libraries are used as a source of ideas for simulation or experimental testing. Another paradigm is generative molecular design, which may suggest novel structures beyond what is available in virtual libraries. I will describe our recent algorithmic approaches with both goals in mind, including the use of model-guided Bayesian optimization for virtual screening. I will discuss new solutions to address common failure modes of deep learning-based generative tools, emphasizing the consideration of synthetic accessibility. Finally, I will describe an optimization framework for the downselection of molecular structures from large candidate sets that strikes a balance between information gain and experimental cost. By providing effective and controllable navigation within synthesizable chemical space, we can provide actionable suggestions of new small organic molecules across a range of fields, including drug development and materials science.

Connor W. Coley is the Class of 1957 Career Development Professor and an Associate Professor without tenure 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 works at the interface of chemistry and data science to develop models that understand how molecules behave, interact, and react and use that knowledge to engineer new ones, with an emphasis on therapeutic discovery. Connor is a recipient of C&EN’s “Talented Twelve” award, Forbes Magazine’s “30 Under 30” for Healthcare, Technology Review’s 35 Innovators Under 35, the NSF CAREER award, the ACS COMP OpenEye Outstanding Junior Faculty Award, the Bayer Early Excellence in Science Award, the 3M NTFA, and was named a Schmidt AI2050 Early Career Fellow, a 2023 Samsung AI Researcher of the Year, and a Scialog Fellow (Automating Chemical Laboratories). Connor has been recognized for his teaching and mentorship by MIT’s inaugural Common Ground Award for Excellence in Teaching, the 2024 Outstanding UROP Mentor Award, and the 2024 James W. Swan Outstanding Faculty Award for Graduate Teaching in Chemical Engineering.

• Time: 11:00 AM
• Location: 206 Furnas Hall