School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.
School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.
J Chem Inf Model. 2022 Jun 27;62(12):2943-2950. doi: 10.1021/acs.jcim.2c00487. Epub 2022 Jun 6.
The ultimate goal of various fields is to directly generate molecules with desired properties, such as water-soluble molecules in drug development and molecules suitable for organic light-emitting diodes or photosensitizers in the field of development of new organic materials. This study proposes a molecular graph generative model based on an autoencoder for the de novo design. The performance of the molecular graph conditional variational autoencoder (MGCVAE) for generating molecules with specific desired properties was investigated by comparing it to a molecular graph variational autoencoder (MGVAE). Furthermore, multi-objective optimization for MGCVAE was applied to satisfy the two selected properties simultaneously. In this study, two physical properties, calculated log and molar refractivity, were used as optimization targets for designing de novo molecules. Consequently, it was confirmed that among the generated molecules, 25.89% of the optimized molecules were generated in MGCVAE compared to 0.66% in MGVAE. This 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 an effective method for designing new molecules that fulfill various physical properties.
各个领域的最终目标是直接生成具有所需性质的分子，例如药物开发中的水溶性分子和新型有机材料开发领域中适合有机发光二极管或光致剂的分子。本研究提出了一种基于自动编码器的分子图生成模型，用于从头设计。通过将分子图条件变分自动编码器（MGCVAE）与分子图变分自动编码器（MGVAE）进行比较，研究了其生成具有特定所需性质的分子的性能。此外，还对 MGCVAE 进行了多目标优化，以同时满足两个选定的性质。在本研究中，将计算的 log 和摩尔折射度这两个物理性质用作设计从头分子的优化目标。结果表明，与 MGVAE 相比，在生成的分子中，MGCVAE 优化生成的分子占 25.89%，而 MGVAE 仅为 0.66%。这表明 MGCVAE 可以有效地生成具有两种目标性质的类药性分子。本研究的结果表明，这些基于图的数据驱动模型是设计满足各种物理性质的新型分子的有效方法。
