MGCVAE: Multi-Objective Inverse Design via Molecular Graph Conditional Variational Autoencoder,Journal of Chemical Information and Modeling

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MGCVAE: Multi-Objective Inverse Design via Molecular Graph Conditional Variational Autoencoder
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-06-06 , DOI: 10.1021/acs.jcim.2c00487
Myeonghun Lee ₁ , Kyoungmin Min ₂

Affiliation

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 logP 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.

中文翻译：

MGCVAE：通过分子图条件变分自动编码器的多目标逆向设计

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有机材料开发领域中适用于有机发光二极管或光敏剂的分子。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。通过将分子图变分自动编码器 (MGVAE) 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分自动编码器 (MGCVAE) 用于生成具有特定所需属性的分子的性能。此外，应用 MGCVAE 的多目标优化来同时满足两个选定的属性。在这项研究中，两个物理性质，计算 log P和摩尔折射率，被用作设计从头分子的优化目标。因此，证实在生成的分子中，MGCVAE 中生成了 25.89% 的优化分子，而 MGVAE 中生成了 0.66%。这表明 MGCVAE 有效地产生了具有两种靶点特性的药物样分子。这项研究的结果表明，这些基于图形的数据驱动模型是设计满足各种物理特性的新分子的有效方法。

更新日期：2022-06-06

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