The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2 = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo . This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.

中文翻译：

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MATEO：分子间α-酰胺烷基化理论对映选择性优化。用于选择和设计手性催化剂和产品的在线工具

对映选择性布朗斯台德酸催化的 α-酰胺烷基化反应是生产新药和天然产物的有用方法。在这种情况下，手性磷酸（CPA）催化剂是此类反应的通用催化剂。用于不同对映选择性反应的新型CPA催化剂的选择和设计具有双重意义，因为可以获得新型CPA催化剂（工具）和手性药物或材料（产品）。然而，如果从实验试错的角度来看，这个过程是困难且耗时的。在这项工作中，使用启发式扰动理论和机器学习 (HPTML) 算法来寻找 CPA 催化剂在 α-酰胺烷基化反应中对映选择性方面性能的预测模型，总体 R2 = 0.96，用于训练和验证系列。它涉及对超过 100,000 对查询和参考反应的蒙特卡洛抽样。此外，还报道了使用 BINOL 衍生的 N-三氟甲基磷酰胺作为 CPA 催化剂对一组新的分子间 α-酰胺烷基化反应进行的计算和实验研究。该模型在名为 MATEO 的网络服务器中实现：分子间酰胺烷基化理论对映选择性优化，可在线获取：https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo。这种新的用户友好的在线计算工具将能够可持续地优化反应条件，从而设计出新的 CPA 催化剂以及新的有机合成产品。