A Quantitative Model for Alkane Nucleophilicity Based on C−H Bond Structural/Topological Descriptors

Abstract

Af irst quantitative model for calculating the nucleophilicity of alkanes is described. As tatistical treatment was applied to the analysis of the reactivity of 29 different alkane CÀHb onds towards in situ generated metal carbene electrophiles.T he correlation of the recently reported experimental reactivity with two different sets of descriptors comprising at otal of 86 parameters was studied, resulting in the quantitative descriptor-based alkane nucleophilicity (QDEAN) model. This model consists of an equation with only six structural/topological descriptors,a nd reproduces the relative reactivity of the alkane CÀHbonds.This reactivity can be calculated from parameters emerging from the schematic drawing of the alkane and asimple set of sums.Theapplicationofstatisticaltechniquestotheunderstanding of chemical problems is emerging as ap owerful tool for the prediction of chemical behavior. [1][2][3][4][5][6] Tw omain approaches are employed depending on the nature of the descriptors used. On the one hand, general descriptors,n ot directly related to the specific problem being evaluated, are employed in freeenergy relationships of widespread use in physical organic chemistry,such as QSAR methods or the Hammett equation. In this context, Sigman and co-workers have developed reaction-oriented descriptors for several organic transformations. [7,8] On the other hand, Aires and co-workers took advantage of statistical techniques to estimate bond dissoci-ation energies (BDEs) for an umber of organic molecules, [9] whereas Jensen, Alsberg, and co-workers automated the design of organometallic molecules. [10,11] Predictive modeling has also been provided by the groups of Rothenberg, [12][13][14] Bo, [15,16] and Paton, [17] while one of our groups,among others, has used multivariate regressions to find free-energy relationships between BDEs in organometallic chemistry. [18][19][20][21] Thea lternative approach is based on the generation of new,problem-specific descriptors from statistical procedures. Feyand co-workers applied [22,23] principal component analysis (PCA)todefine new descriptors and generate adatabase for ligands.S ome of us have successfully used singular value decomposition (SVD) in the search for hidden descriptors governing the metal-ligand BDEs. [24] New descriptors may also be generated by machine-learning techniques. [25][26][27][28][29] Our groups have recently described the first experimental scale of the relative reactivity of carbon-hydrogen bonds in alkanes, [30] with methane as the reference,t owards metal carbene species as organometallic electrophiles (Scheme 1).Herein, we report the statistical treatment of that nucleophilic scale of alkanes,which has led to the development of amodel that replicates the experimental data to avery high degree by using structural characteristics of those CÀHbonds.Our aim is to find ap redictive tool for the challenging,l ow-reactive C À Hb onds of alkanes.Scheme 2s hows the array of 14 alkanes previously evaluated by means of competition experiments using silver...

tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes

“…[9][10][11] Neural networks [12][13][14][15][16] and other ML models have been used successfully in a wide range of applications, with numerous examples in materials science 17-21 and drug discovery. [22][23][24][25][26] ML and data-driven approaches are also making a rapid progress in catalytic, [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] organic, [42][43][44][45][46][47] inorganic 48,49 and theoretical [50][51][52][53][54][55][56] chemistry.…”

The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes

“…[9][10][11] Neural networks [12][13][14][15][16] and other ML models have been used successfully in a wide range of applications, with numerous examples in materials science 17-21 and drug discovery. [22][23][24][25][26] ML and data-driven approaches are also making a rapid progress in catalytic, [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] organic, [42][43][44][45][46][47] inorganic 48,49 and theoretical [50][51][52][53][54][55][56] chemistry.…”