  Recently, molecule generation using deep learning has been actively
investigated in drug discovery. In this field, Transformer and VAE are widely
used as powerful models, but they are rarely used in combination due to
structural and performance mismatch of them. This study proposes a model that
combines these two models through structural and parameter optimization in
handling diverse molecules. The proposed model shows comparable performance to
existing models in generating molecules, and showed by far superior performance
in generating molecules with unseen structures. Another advantage of this VAE
model is that it generates molecules from latent representation, and therefore
properties of molecules can be easily predicted or conditioned with it, and
indeed, we show that the latent representation of the model successfully
predicts molecular properties. Ablation study suggested the advantage of VAE
over other generative models like language model in generating novel molecules.
It also indicated that the latent representation can be shortened to ~32
dimensional variables without loss of reconstruction, suggesting the
possibility of a much smaller molecular descriptor or model than existing ones.
This study is expected to provide a virtual chemical library containing a wide
variety of compounds for virtual screening and to enable efficient screening.
