Using principal component analysis on the lower triangular pairwise nuclear-nuclear distance matrix descriptor should find the variances in bond distances that would indicate significant structural changes. As seen in Figure , the identified clusters represent various stable conformations on the rotational potential energy surface This is reflected in the sampling of five structures closest to the cluster mean. Figure Bond rotations around the CH2 groups near the disubstituted cyclohexane ring can cause potential intramolecular interactions between the two cyclic structures at the end of the structures, which in turn, creates a distribution of unique conformations that can be weighted based on the population of structures in the cluster, i.e., the cluster size. As the dataset size would affect the weighting of the clusters, five-fold cross validation was done for randomized training sets of 2000, 3000, 4000, and 5000 structures (see ESI). The silhouette score analysis and subsequent calculation of the logPtol/w based on the weighted average of various clusters yielded that while 4000 structures provided the most consistent silhouette scores, the MUE of the computed logPtol/w values only marginally improved over the MUE when considering 2000 structures. Therefore, there are several factors of randomness within this ML method that contribute to the predicted logPtol/w.