The naïve training of VAEs consisting of an auto-regressive decoding process often causes the KL vanishing, leading to the poor capture of meaningful information in the input data. As explained by Fu et al., KL vanishing occurs due to an improper way of sequence generation, caused by the generation process relying only on the local context in the decoder while ignoring global features in the VAE. To deal with this issue, a cyclic annealing scheduler was used to adjust the value of β to direct the model to converge towards the training objective and reduce the effect of KL vanishing. We also applied schedulers on other loss terms. A linear scheduler was applied to the loss component L M LP for the reconstruction of BB1 and reaction type within the reconstruction term L recon , inducing a monotonic increase from 0 to 1. The same scheduler was also applied to the property predictor. Our intention in using these two training schedulers was to enable the model to prioritise the reconstruction of the sequence representation of POCs during the initial stages of training, and gradually shift toward the reconstruction of the entire molecule and the organisation of the latent space as the training proceeded.