  The ultimate goal of various fields is to directly generate molecules with
desired properties, such as finding water-soluble molecules in drug development
and finding molecules suitable for organic light-emitting diode (OLED) or
photosensitizers in the field of development of new organic materials. In this
respect, this study proposes a molecular graph generative model based on the
autoencoder for de novo design. The performance of molecular graph conditional
variational autoencoder (MGCVAE) for generating molecules having specific
desired properties is investigated by comparing it to molecular graph
variational autoencoder (MGVAE). Furthermore, multi-objective optimization for
MGCVAE was applied to satisfy two selected properties simultaneously. In this
study, two physical properties -- logP and molar refractivity -- were used as
optimization targets for the purpose of designing de novo molecules, especially
in drug discovery. As a result, it was confirmed that among generated
molecules, 25.89% optimized molecules were generated in MGCVAE compared to
0.66% in MGVAE. Hence, it demonstrates that MGCVAE effectively produced
drug-like molecules with two target properties. The results of this study
suggest that these graph-based data-driven models are one of the effective
methods of designing new molecules that fulfill various physical properties,
such as drug discovery.
