Associate Professor (Without Tenure); Massachusetts Institute of Technology
Connor W. Coley is the Class of 1957 Career Development Professor and an Associate Professor without tenure at MIT. The Coley Research Group works at the interface of chemistry and data science to develop models that understand how molecules behave, interact and react. The group uses the knowledge gained to engineer new molecules with an emphasis on therapeutic discovery.
Dr. Coley has been featured in Clinical & Engineering News’ Talented 12, Forbes’ 30 Under 30 in Healthcare and Technology Review’s 35 Innovators Under 35. He has received the National Science Foundation CAREER award, the American Chemical Society COMP OpenEye Outstanding Junior Faculty Award, the Bayer Early Excellence in Science Award and the 3M Non-Tenured Faculty Award. Dr. Coley was named a Schmidt AI2050 Early Career Fellow, a 2023 Samsung AI Researcher of the Year and a Scialog Fellow (Automating Chemical Laboratories). He has been recognized for his teaching and mentorship by the inaugural MIT 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.
Dr. Coley earned a bachelor of science degree in chemical engineering from the California Institute of Technology in Pasadena, California and a master of science degree in chemical engineering practice and Ph.D. in chemical engineering from the Massachusetts Institute of Technology in Cambridge, Massachusetts. He completed postdoctoral work at the Eli and Edythe L. Broad Institute of MIT and Harvard in Cambridge, Massachusetts.
Open Science Contributions
Learn more about some of Connor’s open science contributions:
- Askcos: Organic chemistry synthesis and planning
- Shepherd: Diffusion generative chemistry
- Molpal: Active learning virtual screening
- DiffMS: Diffusion generation of molecules conditioned on mass spectra
- Graph2SMILES: Model for one-step retrosynthesis and reaction outcome prediction