In summary, this report describes a platform that combines creative human-generated synthetic plans with robust computational analysis for a challenging key step. Machine learning models are trained to predict the yields of reactions from diverse literature examples. A neural network model was used to guide the retrosynthetic analysis of several sesquiterpenoid natural products, resulting in their highly efficient syntheses. We expect that models for other transformations could be developed following this workflow, which would allow for evaluation of retrosynthetic plans with varying key transformations. Moreover, the success of this strategy argues for broader use of computational tools as part of the process for synthetic planning.