where the L KL term represents the Kullback-Leibler (KL) divergence between the prior distribution p(z) and the learnt posterior distribution, q ϕ (z|X). Here, the prior is assumed as the standard normal distribution. β is introduced to balance the expected reconstruction loss L Recon and the KL term L KL . The auto-regressive gated recurrent unit (GRU) was employed in both encoder and decoder architectures attributed to the sequence part processing for the BB2 skeletons of the cage encoding. A bidirectional version of a GRU was adopted in the encoder for capturing the information of SMILES sequences in both the forward and backward directions. A singledirectional GRU was used to decode SMILES sequences from the latent space. The BB1s and reaction types of the cage assembly are processed as combined vectors in an encoder-decoder pair using multi-layer perceptrons (MLP). It should be noted that the generation of either BB1 skeletons or reaction types is not beyond the existing categories in the original and augmented datasets due to the use of ordinal encodings. Encoders are responsible for jointly encoding components of cages to the continuous latent space z, and decoders reconstruct corresponding cage components from the latent space. A property predictor based on an MLP was then coupled to the VAE to predict the property from the latent space. The VAE was jointly trained with the property predictor to impose a property-based bias on the distribution of the embedded cages in the latent space.