论文标题
MGCVAE：通过分子图条件变异自动编码器的多目标逆设计
MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder
论文作者
论文摘要
各个领域的最终目标是直接生成具有所需特性的分子，例如在药物开发中找到水溶性分子，并找到适合有机发光二极管（OLED）的分子，或在新有机材料开发领域中的光敏剂。在这方面，这项研究提出了基于自动编码器从头设计的分子图生成模型。通过将其与分子图变分自动编码器（MGVAE）进行比较，研究了分子图条件变异自动编码器（MGCVAE）生成具有特定所需特性的分子的性能。此外，对MGCVAE进行多目标优化，以同时满足两个选定的特性。在这项研究中，将两种物理特性（LOGP和摩尔折射率）用作设计从头分子的目的，尤其是在药物发现中。结果，已经证实，在产生的分子中，在MGCVAE中产生了25.89％的优化分子，而MGVAE则为0.66％。因此，它表明MGCVAE有效地产生了具有两个靶性能的药物样分子。这项研究的结果表明，这些基于图的数据驱动模型是设计新分子的有效方法之一，这些分子符合各种物理特性，例如药物发现。
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.
