Kanematsu Yusuke, Ohta Akiyoshi, Nagai Shunya, Adachi Yohei, Kaneko Hiromasa, Ishimoto Takayoshi, Kurita Takio, Ohshita Joji
Smart Innovation Program, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan.
Division of Materials Model-Based Research, Digital Monozukuri (Manufacturing) Education and Research Center, Hiroshima University, Higashi-Hiroshima 739-0046, Japan.
Molecules. 2025 Apr 10;30(8):1686. doi: 10.3390/molecules30081686.
We have built a prediction model of the fluorescence quantum yields of metalloles. Based on the suggestion by the prediction model, we synthesized 10 fluorescent molecules to confirm the prediction accuracy. By measuring the fluorescence quantum yields of the synthesized molecules, it was demonstrated that our prediction model reasonably classified the quantum yields with an accuracy of 0.7. In particular, the low quantum yields were perfectly predicted for the synthesized molecules, demonstrating the usefulness of our prediction model to screen out weakly fluorescent molecules from the candidates. On the other hand, the low precision of 0.5 was attributed to the bias in the training dataset containing many fluorine-containing molecules with high quantum yields. Our prediction model was then revised with the generator of candidate molecular structures for more efficient development of fluorescent materials with taking the applicability domain into account, and the improvement of the applicability was confirmed owing to the increment of the dataset.
我们构建了一个金属杂环化合物荧光量子产率的预测模型。基于该预测模型的建议，我们合成了10种荧光分子以确认预测准确性。通过测量合成分子的荧光量子产率，结果表明我们的预测模型能够以0.7的准确率合理地对量子产率进行分类。特别是，对于合成分子，低量子产率得到了完美预测，这证明了我们的预测模型在从候选物中筛选出弱荧光分子方面的有用性。另一方面，0.5的低精度归因于训练数据集中存在偏差，该数据集中包含许多具有高量子产率的含氟分子。然后，我们的预测模型使用候选分子结构生成器进行了修订，以便在考虑适用范围的情况下更有效地开发荧光材料，并且由于数据集的增加，确认了适用性的提高。
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