A neural network model informs the total synthesis of clovane sesquiterpenoids

Efficient syntheses of complex small molecules, such as bioactive natural products, often involve detailed retrosynthetic planning and experimental evaluation of speculative synthetic routes. The central challenge of such an approach is that experimental evaluation of high-risk strategies is resource intensive because it requires iterative attempts at unsuccessful strategies. Along with the rapid development of cheminformatics and artificial intelligence, computer-aided synthetic planning has emerged to address this challenge. Herein, we report a complementary strategy that combines human-generated synthetic plans with computational prediction of the feasibility of key steps in the proposed synthesis. A neural network model (NNET) was trained on a literature-based dataset (from Reaxys) to predict the outcome of a generally disfavoured transformation, 6- endo - trig radical cyclization. The model performance was rigorously tested by experimental validation. On the basis of the virtual screening of potential substrates with our NNET model, optimal disconnections and structural modifications were chosen, resulting in five- to eight-step syntheses of three clovane sesquiterpenoids. This work establishes how a machine learning model informs human design and guides multistep syntheses of complex small molecules.