Data Science-Guided Development of Deoxyfluorination Reagents with Enhanced Reactivity, Practicality, and Safety
Madeline E. Ruos, Natalie P. Romer, Julie A. Deichert, Lady Mae Alabanza, Shivaani S. Gandhi, Giselle Z. Brown, Richard C. Walroth, Karissa Cruz, Francis Gosselin, Allen Y. Hong, Matthew S. Sigman, Abigail G. Doyle
Abstract
We report the discovery and development of several new (hetero)aryl sulfonyl fluoride reagents that have enhanced deoxyfluorination reactivity, improved physical properties, and excellent safety profiles compared to those of PyFluor and other fluorination reagents such as PBSF and DAST. To select structurally diverse reagents, we computed a virtual library of (hetero)aryl sulfonyl fluorides and leveraged training set design principles to broadly survey structure-activity relationships in a model deoxyfluorination reaction. We developed predictive models to optimize sulfonyl fluoride reagents for the deoxyfluorination of a key intermediate used in the synthesis of RIPK1 inhibitor GDC-8264. The top-performing reagents demonstrated broad applicability across diverse alcohol substrate classes, including complex natural products and active pharmaceutical ingredients, highlighting the power of data science-enabled approaches in reagent development.