Though VAE can organise the latent space by intrinsic characteristics of molecular representations, the latent space can also be effectively organised by properties mapped by an external predictor. Unlike the previous examples that used continuous targets as the signal, our property of interest, cage shape persistence, is a discrete classification. The MLP-based property predictor jointly trained with the VAE can achieve a test accuracy of 83.1% in shape persistence prediction. This suggests that the predictor captures the feature of shape persistence of molecules represented by latent vectors and therefore makes it possible for the predictor to correctly organise the latent space.