Nagano Taiki, Bagal Dattatraya B, Kunisada Ryuga, Nakajima Haruno, Sano Koki, Noto Naoki, Saito Susumu
Graduate School of Science, Nagoya University, Nagoya, 464-8602, Japan.
Research Center for Materials Science, Nagoya University, Nagoya, 464-8602, Japan.
Chemistry. 2025 Aug 1;31(43):e202500955. doi: 10.1002/chem.202500955. Epub 2025 Jun 11.
Herein, we report that the catalytic activity of phosphorus compounds with a P─H bond in dehydrative amidation reactions is substantially enhanced when using catechol derivatives as additives. A systematic investigation into the catalytic activity of four phosphorus compounds and 17 catechol derivatives allowed identifying several effective combinations, for example, dimethyl phosphite and 2,3-dihydroxynaphthalene, for the catalytic amidation of carboxylic acids with amines. The developed catalytic system enables the synthesis of pharmaceuticals and facilitates the amidation of anilines, which are typically considered poor substrates with low reactivity. In addition, machine-learning (ML) models were constructed to validate the effectiveness of the database generated from the above-mentioned investigation. Multilayer perceptron (MLP) models incorporating descriptors derived from both quantum chemical calculations and RDKit provided accurate predictions while maintaining chemical interpretability, which was underscored by a visual analysis using SHAP.
在此，我们报道，当使用邻苯二酚衍生物作为添加剂时，含P─H键的磷化合物在脱水酰胺化反应中的催化活性会显著增强。对四种磷化合物和17种邻苯二酚衍生物的催化活性进行系统研究，从而确定了几种有效的组合，例如亚磷酸二甲酯和2,3-二羟基萘，用于羧酸与胺的催化酰胺化反应。所开发的催化体系能够实现药物的合成，并促进苯胺的酰胺化反应，而苯胺通常被认为是反应活性较低的不良底物。此外，构建了机器学习（ML）模型以验证从上述研究所生成数据库的有效性。包含源自量子化学计算和RDKit的描述符的多层感知器（MLP）模型在保持化学可解释性的同时提供了准确的预测，使用SHAP进行的可视化分析突出了这一点。
