GOALI: Developing Novel Photocatalytic Methods to Address Synthetic Limitations in the Pharmaceutical Industry

With the support of the Chemical Catalysis Program in the Division of Chemistry, Professor Tomislav Rovis of Columbia University is studying the development of new chemical reactions to solve challenging problems that pharmaceutical scientists encounter in collaboration with Bristol Myers Squibb (BMS) researchers. New therapeutics contain increasingly complex chemical structures that necessitate innovative methods to construct them. Chemists doing drug discovery research must either make these complex molecules through longer routes, or choose to make alternate, less desirable structures. Prof. Rovis and the BMS team are using catalysts activated by low energy orange light to drive chemical reactions that promise to be more tolerant of reactive functionality and complex structures, facilitating the assembly of molecules having desirable biological properties. This project is also facilitating the scientific interactions and exchange of ideas between BMS scientists and Columbia graduate students, with regular research updates and quarterly reciprocal visits strengthening relationships while exposing junior scientists to industrial science. These activities are aiding chemists everywhere to make more complex therapeutics of tomorrow. Reactions that forge alkyl-aryl carbon-carbon bonds are of high importance in modern drug design to prepare compounds with improved physicochemical properties through shorter synthetic routes. Prof. Rovis and his research group are collaborating with BMS to design catalytic systems that use visible light to drive these reactions. By harnessing novel catalysts, lower energy orange light (560-600 nm) is being used to couple two distinct components to make a new carbon-carbon bond under mild and potentially more tolerant reaction conditions. In an orthogonal approach, the team is using cheap and widely available copper catalysts to make an emerging functional group increasingly found in bioactive compounds by facilitating a challenging reductive elimination. This transformation is also driven by visible light, translating the energy in a photon to accelerate a desired bond construction. Lastly, a machine learning platform is being developed and exploited to predict optimal catalytic conditions for carbon-carbon bond formation in photocatalytic systems using acetyl acetonate ligands. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.