Figure 3.

Two-dimensional PCA analysis of latent space for variational autoencoder. The two axis are the principle components selected from the PCA analysis; the color bar shows the value of the selected property. The first column shows the representation of all molecules from the listed data set using autoencoders trained without joint property prediction. The second column shows the representation of molecules using an autoencoder trained with joint property prediction. The third column shows a representation of random points in the latent space of the autoencoder trained with joint property prediction; the property values predicted for these points are predicted using the property predictor network. The first three rows show the results of training on molecules from the ZINC data set for the logP, QED, and SAS properties; the last two rows show the results of training on the QM9 data set for the LUMO energy and the electronic spatial extent (R²).