It has been shown that the simultaneous training of a property predictor on a latent space leads to a desirable "ordering" of that space visually identified in the top principal components. Although there are many such dimensionality reduction techniques available, we employ PCA here because it provides an interpretable and invertible mapping to a visualizationfriendly space and has been previously used in the field of de novo molecular generationGómez-Bombarelli et al. . In this section, we identify ordering in the top principal components (PCs) in our models and go one step further by quantifying the extent of the ordering and the PCs in which it appears.