SI02_MATEO Ref Recctions.xlsx

In this Figsahre repository we release the dataset used to train/validate the MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization Web Server. SI File: <strong>SI02_MATEO Ref Recctions.xlsx</strong> Content: Data of reactions of reference uploaded to the internal database of the web server. <br> Summary: The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products (products) or chiral catalysts (tools). The enantioselectivity is sensitive to many factors, from the nature of the nucleophile and the catalyst to the experimental conditions (solvent, temperature, etc.). Although computational chemistry has been used to rationalize experimental results, it is still difficult to understand the influence of different parameters (solvent, temperature, etc.) on the quantitative reaction outcome (as yield or regio- and stereoselectivities).Both experimental and computational (Quantum Chemistry) study of a large number of reactions may become costly in terms of resources and time. Thus, the development of fast-track public computational tools to predict the enantioselectivity [enantiomeric excess <em>ee</em>(%)obs] would be very useful. Furthermore, making the new tool available online could save time and experimental resources in many labs worldwide. We used an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm to seek a predictive model with R2 = 0.91 in training and validation series has been developed. It involves a Monte Carlo sampling of&gt;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 <em>N</em>-trifylphosphoramides as chiral catalysts is reported as a case of study. After validation of the model, it was implementedin a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization. This tool is available online at:https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo.This new user-friendly online computational tool may become useful to explore a large number of combinations of reactants, catalysts, and experimental conditions. This public tool would enable sustainable optimization of reaction conditions that could lead to the design of new catalysts, substrates, nucleophiles, and/or products.

本Figsahre仓库发布了用于训练与验证MATEO：分子间酰胺烷基化理论对映选择性优化网络服务器的数据集。补充材料文件：<strong>SI02_MATEO 参比反应.xlsx</strong>，内容为上传至该网络服务器内部数据库的参比反应数据。
摘要：对映选择性布朗斯特酸（Brønsted acid）催化的α-酰胺烷基化反应，是制备新型药物、天然产物（目标产物）或手性催化剂（工具底物）的实用合成方法。对映选择性受诸多因素影响，涵盖亲核试剂与催化剂的性质，以及实验条件（溶剂、温度等）。尽管计算化学已被用于阐释实验结果，但厘清不同参数（溶剂、温度等）对反应定量结果（如产率、区域选择性与立体选择性）的影响仍颇具挑战。
针对大量反应开展实验与量子化学（Quantum Chemistry）研究，在资源与时间层面的投入成本均较高。因此，开发快速可用的公开计算工具以预测对映选择性[对映体过量值（enantiomeric excess, <em>ee</em>）(%)观测值]将极具实用价值。此外，将该新型工具上线发布，可为全球众多实验室节省时间与实验资源。
本研究采用启发式微扰理论与机器学习（Heuristic Perturbation-Theory and Machine Learning, HPTML）算法开发了一款预测模型，其在训练与验证集上的决定系数（R²）可达0.91。该算法包含对10万余对查询反应与参比反应的蒙特卡洛（Monte Carlo）采样。
此外，本研究还报道了一组以联萘酚（BINOL）衍生的N-三氟甲磺酰基磷酰胺作为手性催化剂的新型分子间α-酰胺烷基化反应，作为案例研究开展计算与实验考察。
经模型验证后，该模型被部署至名为MATEO：分子间酰胺烷基化理论对映选择性优化的网络服务器。该工具的在线访问地址为：https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo。
这款用户友好型的公开在线计算工具，可用于探索反应物、催化剂与实验条件的大量组合。该公开工具将助力反应条件的可持续优化，进而推动新型催化剂、底物、亲核试剂及/或目标产物的设计开发。