  In data-driven drug discovery, designing molecular descriptors is a very
important task. Deep generative models such as variational autoencoders (VAEs)
offer a potential solution by designing descriptors as probabilistic latent
vectors derived from molecular structures. These models can be trained on large
datasets, which have only molecular structures, and applied to transfer
learning. Nevertheless, the approximate posterior distribution of the latent
vectors of the usual VAE assumes a simple multivariate Gaussian distribution
with zero covariance, which may limit the performance of representing the
latent features. To overcome this limitation, we propose a novel molecular deep
generative model that incorporates a hierarchical structure into the
probabilistic latent vectors. We achieve this by a denoising diffusion
probabilistic model (DDPM). We demonstrate that our model can design effective
molecular latent vectors for molecular property prediction from some
experiments by small datasets on physical properties and activity. The results
highlight the superior prediction performance and robustness of our model
compared to existing approaches.
