  Deep generative models are attracting great attention as a new promising
approach for molecular design. All models reported so far are based on either
variational autoencoder (VAE) or generative adversarial network (GAN). Here we
propose a new type model based on an adversarially regularized autoencoder
(ARAE). It basically uses latent variables like VAE, but the distribution of
the latent variables is obtained by adversarial training like in GAN. The
latter is intended to avoid both inappropriate approximation of posterior
distribution in VAE and difficulty in handling discrete variables in GAN. Our
benchmark study showed that ARAE indeed outperformed conventional models in
terms of validity, uniqueness, and novelty per generated molecule. We also
demonstrated successful conditional generation of drug-like molecules with ARAE
for both cases of single and multiple properties control. As a potential
real-world application, we could generate EGFR inhibitors sharing the scaffolds
of known active molecules while satisfying drug-like conditions simultaneously.
