Tomasini Michele, Voccia Maria, Caporaso Lucia, Szostak Michal, Poater Albert
Institut de Química Computacional i Catàlisi, Departament de Química, Universitat de Girona c/Ma Aurèlia Capmany 69 17003 Girona Catalonia Spain
Dipartimento di Chimica e Biologia, Università di Salerno Via Ponte don Melillo 84084 Fisciano Italy.
Chem Sci. 2024 Jul 11;15(33):13405-13414. doi: 10.1039/d4sc03873h. eCollection 2024 Aug 22.
Amines are one of the most prevalent functional groups in chemistry. Perhaps even more importantly, amines represent one of the most ubiquitous moieties within the realm of bioactive natural products and life-saving pharmaceuticals. The archetypal geometrical property of amines is their sp hybridization with the lone pair of nitrogen occupying the apex of the pyramid. Herein, we present a blueprint for quantifying the properties of extremely sterically hindered alkylamines. These amines reach planarity around the nitrogen atom due to the excessive steric hindrance, which results in a conformational re-modeling of the amine moiety. Crucially, the steric properties of amines are characterized by the % index, which we show is a general predictive parameter for evaluating the properties of sterically hindered amines. Computational studies on the acidic nature and the reactivity of organometallic Au and Pd complexes are outlined. Density functional theory calculations permit for predictive catalysis, ordering the mapping of extremely hindered tertiary amines by employing artificial intelligence machine learning. Overall, the study outlines the correlation between the unusual geometry and the key thermodynamic and kinetic properties of extremely hindered alkylamines. The steric hindrance, as quantified by % , is the crucial factor influencing the observed trends and the space required to accommodate sterically hindered tertiary amines.
胺是化学中最普遍存在的官能团之一。或许更重要的是,胺是生物活性天然产物和救命药物领域中最常见的基团之一。胺的典型几何性质是其sp杂化,孤对氮占据金字塔的顶点。在此,我们提出了一种量化极度空间位阻烷基胺性质的蓝图。由于过度的空间位阻,这些胺在氮原子周围达到平面性,这导致胺部分的构象重新建模。至关重要的是,胺的空间性质由%指数表征,我们表明它是评估空间位阻胺性质的一个通用预测参数。概述了关于有机金属金和钯配合物的酸性性质和反应性的计算研究。密度泛函理论计算允许进行预测催化,通过使用人工智能机器学习对极度受阻叔胺进行排序映射。总体而言,该研究概述了极度受阻烷基胺的异常几何结构与关键热力学和动力学性质之间的相关性。由%量化的空间位阻是影响观察到的趋势和容纳空间位阻叔胺所需空间的关键因素。