Radical cyclizations constitute a powerful method for the construction of hindered or strained ring systems and are commonly employed in complex molecule synthesis . However, the utility of some radical cyclizations, including often unfavorable 6-endo cyclizations, can be limited by the difficulty of predicting their outcome. Baldwin's and Beckwith's rules and other methods of analysis can in some cases suggest trends for related systems, but cannot quantitatively inform the outcome of diverse proposed transformations. A more sophisticated prediction of synthetic feasibility is enabled by the machine learning (ML) model described herein, which was applied to the synthetic planning of clovane sesquiterpenoids as a proof-of-concept of this approach. The clovanes share a common tricyclic bridged-ring skeleton with three quaternary centers, and have been a subject of synthesis since the 1960s . Additionally, clovanes exist widely in both terrestrial and marine organisms and show diverse biological activities: for example, clovan-2,9-dione (1) and rumphellclovane B (3) inhibit production of superoxide anion and inhibit elastase released by human neutrophils ; clovanemagnolol (6) exhibits excellent neurotrophic activity at concentrations of 10 nM . Stunning biomimetic semi-syntheses have been reported of clovanes , but semi-synthetic approaches provide limited opportunity for deep-seated structural modifications and concise access to related materials . Our de novo synthesis reported herein provides flexible entry and access to diverse clovanes that complement those available from semi-synthetic approaches.