Fujita Takehiro, Terayama Kei, Sumita Masato, Tamura Ryo, Nakamura Yasuyuki, Naito Masanobu, Tsuda Koji
Polymer Design Group, Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS)Data-driven, Tsukuba, Japan.
Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Japan.
Sci Technol Adv Mater. 2022 Jun 1;23(1):352-360. doi: 10.1080/14686996.2022.2075240. eCollection 2022.
Recently, artificial intelligence (AI)-enabled de novo molecular generators (DNMGs) have automated molecular design based on data-driven or simulation-based property estimates. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of molecule optimization, we propose an algorithm called characteristic functional group monitoring (CFGM). Given a time series of generated molecules, CFGM monitors statistically enriched functional groups in comparison to the training data. In the task of absorption wavelength maximization of pure organic molecules (consisting of H, C, N, and O), we successfully identified a strategic change from diketone and aniline derivatives to quinone derivatives. In addition, CFGM led us to a hypothesis that 1,2-quinone is an unconventional chromophore, which was verified with chemical synthesis. This study shows the possibility that human experts can learn from DNMGs to expand their ability to discover functional molecules.
最近，基于人工智能（AI）的从头分子生成器（DNMGs）已根据数据驱动或基于模拟的性质估计实现了分子设计自动化。在一些领域，如人工智能超越人类智能的围棋游戏中，人类正试图从人工智能中学习游戏的最佳策略。为了理解DNMGs优化分子的策略，我们提出了一种名为特征官能团监测（CFGM）的算法。给定一系列生成的分子，CFGM会与训练数据相比，监测统计上富集的官能团。在纯有机分子（由H、C、N和O组成）吸收波长最大化的任务中，我们成功地识别出了从二酮和苯胺衍生物到醌衍生物的策略转变。此外，CFGM引导我们提出了一个假设，即1,2 - 醌是一种非常规发色团，这一假设通过化学合成得到了验证。这项研究表明，人类专家有可能从DNMGs中学习，以扩展他们发现功能分子的能力。
