A Neural Network Model Informs Total Synthesis of Clovane Sesquiterpenoids

Efficient syntheses of complex small molecules often involve speculative experimental approaches. The central challenge of such plans is that experimental evaluation of high-risk strategies is resource intensive, as it entails iterative attempts at unsuccessful strategies. Herein, we report a complementary strategy that combines creative human-generated synthetic plans with robust computational prediction of the feasibility of key steps in the proposed synthesis. A neural network model was developed to predict the outcome of a generally disfavored transformation, the 6-endo-trig radical cyclization, and applied to synthetic planning of clovan-2,9-dione, resulting in a 5-step total synthesis that improves on a prior 15-step approach. This work establishes how machine learning can guide multistep syntheses that employ innovative and high-risk human-generated plans.