Understanding Stereoinductionin Catalysis via Computer: New Tools for Asymmetric Synthesis

Abstract

An overview of new concepts, new algorithms, and new computational protocols directed at synthetic chemists interested in generating chiral catalysts is presented in this report. A new mapping tool called stereocartography is described, which allows one to locate the most stereoinducing region around a proposed catalyst. Extant QSAR methods including Comparative Molecular Field Analysis (CoMFA) and a new technique called QM-QSAR are tools for modeling stereoinduction and for making predictions about whether a proposed chiral catalyst will be effective or not. Database mining with the intention of locating unbiased, unique ligand motifs for use in catalysis is described. Functionality mapping of transition states is introduced with the idea of locating where around that transition structure, functional groups assist or interfere with a given reaction coordinate. Functionality mapping in combination with database mining is shown to be an effective way to generate new catalysts for asymmetric induction. Beyond these new tools, more advanced treatments of chirality are investigated -the relationship between the chirality content of a catalyst and its ability to induce asymmetry is discussed along with the concept of distorting a ligand to make it more or less chiral. Chirality Content and Stereoinduction 2.8 Chirality Content and Ligand Distortion 3Future Prospects

A novel approach for investigating enantioselectivity in asymmetric hydrogenation

“…To the best of our knowledge, there is little guidance in the literature as to where in any quandrant the enantiodetermining groups should be during a hydrogenation so as to maximize enantioselectivity. 4 Moreover there is no one ligand that gives high enantioselectivity for any substrate, that is, each substrate has its own ligand of choice. We reasoned that a substrate must have its best location (or 'sweet spot'), at which maximum shielding in the quandrant occurs leading to high selectivity.…”

Evaluating continuous chirality measure as a 3D descriptor in chemoinformatics applied to asymmetric catalysis

“…The experimental enantioselectivity data is given in Scheme 2. The CCM values of catalysts were calculated using [1]: CCM_LE, (2) CCM_ C 2 -H, (3) CCM_ C 2 -A, and (4) CCM_Chiraphore. A summary of results is presented in Fig.…”

“…Whereas the intuition of a skilled experimentalist is still valued, even the most experienced veteran is incapable of analyzing vast quantities of data and identifying the multidimensional relationships pertaining to catalyst efficacy. To address these inherent limitations, synthetic chemists have embraced the use of computational tools for catalyst design, as reflected in the numerous reviews in the area [1][2][3][4][5][6][7][8][9][10][11][12]. Of these, chemoinformatics provides an attractive approach to catalyst development for several reasons: (1) no mechanistic information is needed, (2) catalyst structures can be characterized by 3D-descriptors (numerical representations of molecular properties derived from the 3-D structure of the molecule) which quantify the steric and electronic properties of thousands of candidate molecules, and (3) the suitability of a catalyst candidate can be quantified by comparing its properties to a computationally derived model on the basis of experimental data [13][14][15].…”

QSAR models based on isomorphic and nonisomorphic data fusion for predicting the blood brain barrier permeability

“…Quantitative structure-property/activity relationships (QSPR/QSAR) assist via computer the study and synthesis of new drugs, in addition to other substances of fine chemistry (efficient catalysts, selective adsorbents, etc.). [1][2][3][4] After training and validating the QSPR/QSAR models according to their accuracy and robustness levels, descriptors, which reflect chemical characteristics of a structure, can be related to its properties/activities through mathematical functions. Advantages of QSPR/QSAR models are those related to the optimization of chemical processes regarding to residues generation, time, resources involved, etc.…”