The feasibility of using machine learning to enable the total synthesis of clovanes is supported by complementary research in synthetic methods development using chemoinformatics . These workflows inspired our efforts, but none of them could be directly applied to complex molecule synthesis. The major differences are summarized here: (a) the substrates used in synthetic methodology development are readily available, whereas substrates involved in complex molecule synthesis require time consuming multistep synthetic operations to obtain; (b) similar substrates, ligands, or catalysts often appear in multiple instances throughout the libraries used for synthetic methodology, which cover a relatively narrow region of chemical space, whereas the substrates and products in our radical cyclization library are highly diverse; (c) the datasets generated from a single source (i.e. high-throughput experimentation) or a small number of literature references are relatively homogenous, whereas our datasets are derived from highly heterogenous sources with considerable variability in protocol and reaction conditions. Although a purely DFT approach was successful for substrate selection in the case of the total synthesis of paspaline A and emindole PB , methods that evaluate energies of products and transition states would be challenging for this radical cyclization due to a complex interrelationship between kinetics and thermodynamics. As can be seen in Figure , the 5-exo mode of cyclization is kinetically favored whereas the desired 6-endo radical cyclization 10 intermediate is thermodynamically favored; it was unknown if greater thermodynamic preference (ΔG rxn ) would result in higher yield of the 6-endo product . To investigate this possibility, the experimental yields of 125 literature reactions were plotted against their computed free energies of reaction (ΔG rxn ). The lack of a correlation suggests that yield is determined by many factors besides ΔG rxn . It was thus hypothesized that a multiparameter ML model would allow for accurate yield predictions of 6-endo-trig radical cyclizations, which was needed to evaluate synthetic feasibility.