MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder
This work addresses the need for efficient multi-objective inverse design of molecules in fields like drug discovery, though it is incremental as it builds on existing graph-based generative models.
The study tackled the problem of generating molecules with specific desired properties by proposing MGCVAE, a molecular graph conditional variational autoencoder, and achieved a significant improvement, with 25.89% of generated molecules meeting two target properties compared to 0.66% for a baseline model.
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.