2022/02/14 by Myeonghun Lee, Kyoungmin Min, Lee, Myeonghun +1 · 1 citation
Computer Science · Environmental Science · Materials Science · #Chemical Physics (physics.chem-ph) #Chemistry and Chemical Engineering #Computational Drug Discovery Methods #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2202.07476
openalex publication_date 2022/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.