Feature importance analysis (Figure ) on the RF model revealed that linker and repeat unit chemistry at the first position are the most important features, which is consistent with the manually extracted design rules discussed in the context of the MD results. Linear correlations are present among length-related features and among planarity-related features (Figure ), but there are otherwise limited correlations among features. The EXP-1D and EXP-2D series were used as an additional external test set. The RF model correctly predicted all 50 EXP-2D ligands to be stable but misclassified the six EXP-1D ligands. To test if this reflected data limitations, an RF model was trained with the same procedure but with the EXP series included during training. The retrained model achieved 100% training accuracy and 98.7% testing accuracy, which confirmed that the present featurization is sufficient to distinguish these cases but would benefit from a greater diversity of unstable training samples. Nevertheless, the achievement of high accuracy with only nine simple features illustrates the opportunities for accelerating ligand exploration using machine learning models.