INTRODUCTION The development of new synthetic methods in organic chemistry is traditionally accomplished through empirical optimization. Catalyst design, wherein experimentalists attempt to qualitatively identify correlations between catalyst structure and catalyst efficiency, is no exception. However, this approach is plagued by numerous deficiencies, including the lack of mechanistic understanding of a new transformation, the inherent limitations of human cognitive abilities to find patterns in large collections of data, and the lack of quantitative guidelines to aid catalyst identification. Chemoinformatics provides an attractive alternative to empiricism for several reasons: Mechanistic information is not a prerequisite, catalyst structures can be characterized by three-dimensional (3D) descriptors (numerical representations of molecular properties derived from the 3D molecular structure) that quantify the steric and electronic properties of thousands of candidate molecules, and the suitability of a given catalyst candidate can be quantified by comparing its properties with a computationally derived model trained on experimental data. The ability to accurately predict a selective catalyst by using a set of less than optimal data remains a major goal for machine learning with respect to asymmetric catalysis. We report a method to achieve this goal and propose a more efficient alternative to traditional catalyst design. RATIONALE The workflow we have created consists of the following components: (i) construction of an in silico library comprising a large collection of conceivable, synthetically accessible catalysts derived from a particular scaffold; (ii) calculation of relevant chemical descriptors for each scaffold; (iii) selection of a representative subset of the catalysts [this subset is termed the universal training set (UTS) because it is agnostic to reaction or mechanism and thus can be used to optimize any reaction catalyzed by that scaffold]; (iv) collection of the training data; and (v) application of machine learning methods to generate models that predict the enantioselectivity of each member of the in silico library. These models are evaluated with an external test set of catalysts (predicting selectivities of catalysts outside of the training data). The validated models can then be used to select the optimal catalyst for a given reaction. RESULTS To demonstrate the viability of our method, we predicted reaction outcomes with substrate combinations and catalysts different from the training data and simulated a situation in which highly selective reactions had not been achieved. In the first demonstration, a model was constructed by using support vector machines and validated with three different external test sets. The first test set evaluated the ability of the model to predict the selectivity of only reactions forming new products with catalysts from the training set. The model performed well, with a mean absolute deviation (MAD) of 0.161 kcal/mol. Next, the same model was used to predict the selectivity of an external test set of catalysts with substrate combinations from the training set. The performance of the model was still highly accurate, with a MAD of 0.211 kcal/mol. Lastly, reactions forming new products with the external test catalysts were predicted with a MAD of 0.236 kcal/mol. In the second study, no reactions with selectivity above 80% enantiomeric excess were used as training data. Deep feed-forward neural networks accurately reproduced the experimental selectivity data, successfully predicting the most selective reactions. More notably, the general trends in selectivity, on the basis of average catalyst selectivity, were correctly identified. Despite omitting about half of the experimental free energy range from the training data, we could still make accurate predictions in this region of selectivity space. CONCLUSION The capability to predict selective catalysts has the potential to change the way chemists select and optimize chiral catalysts from an empirically guided to a mathematically guided approach.

Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Enantioselective Catalysis by Chiral Isothioureas

Enantioselective copper(I) hydride (CuH)-catalyzed hydroamination has undergone significant development over the past several years. To gain a general understanding of the factors governing these reactions, kinetic and spectroscopic studies were performed on the CuH-catalyzed hydroamination of styrene. Reaction profile analysis, rate order assessment, and Hammett studies indicate that the turnover-limiting step is regeneration of the CuH catalyst by reaction with a silane, with a phosphine-ligated copper(I) benzoate as the catalyst resting state. Spectroscopic, electrospray ionization mass spectrometry, and nonlinear effect studies are consistent with a monomeric active catalyst. With this insight, targeted reagent optimization led to the development of an optimized protocol with an operationally simple setup (ligated copper(II) precatalyst, open to air) and short reaction times (<30 min). This improved protocol is amenable to a diverse range of alkene and alkyne substrate classes.

http://pubs.acs.org/doi/pdf/10.1021/jacs.5b10219

Mechanistic Studies Lead to Dramatically Improved Reaction Conditions for the Cu-Catalyzed Asymmetric Hydroamination of Olefins.

The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

/pdf/quantitative-structure-selectivity-relationships-in-1sfsvo1zok.pdf

Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

A polystyrene-supported isothiourea (1a) behaves as a highly efficient organocatalyst in a variety of formal [4 + 2] cycloaddition reactions. The catalytic system has proven to be highly versatile, leading to six-membered heterocycles and spiro-heterocycles bearing an oxindole moiety in high yields and very high enantioselectivities (32 examples, including the previously unreported oxindole spiropyranopyrazolones 8; 97% mean ee). The notable chemical stability of 1a under operation conditions results in high recyclability (11 cycles, accumulated TON of 76.8) and allows the implementation of an extended-operation continuous flow process (no decrease in yield or ee after 18 h).

/pdf/asymmetric-4-2-annulation-reactions-catalyzed-by-a-robust-3xc6bpcr9s.pdf

Asymmetric [4 + 2] Annulation Reactions Catalyzed by a Robust, Immobilized Isothiourea

The substituent effect of different p-substituted triphenylsilyl chlorides on silylation-based kinetic resolutions was explored. Electron-donating groups slow down the reaction rate and improve the selectivity, while electron-withdrawing groups increase the reaction rate and decrease the selectivity. Linear free-energy relationships were found correlating both selectivity factors and initial rates to the σpara Hammett parameters. A weak correlation of selectivity factors to Charton values was also observed when just alkyl substituents were employed but was nonexistent when substituents with more electronic effects were incorporated. The rate data suggest that a significant redistribution of charge occurs in the transition state, with an overall decrease in positive charge. The linear free-energy relationship derived from selectivity factors is best understood by the Hammond postulate. Early and late transition states describe the amount of substrate participation in the transition state and therefore the ...

Linear free-energy relationship and rate study on a silylation-based kinetic resolution: mechanistic insights.

Diastereoselective and enantioselective silylation of 2-arylcyclohexanols.

A silyl chloride derivatized styrene polymer was employed in the silylation-based kinetic resolution of secondary alcohols for chromatography-free separation of alcohol enantiomers. Synthetically useful selectivity factors were obtained; furthermore, the polymer was recycled for use in a subsequent kinetic resolution, and it maintained its selectivity and integrity.

Polystyrene-Supported Triphenylsilyl Chloride for the Silylation-Based Kinetic Resolution of Secondary Alcohols

Incorporation of hexafluorobutyne into furans or phenols via reaction with iron(0) carbene or vinylketene complexes.

http://myweb.liu.edu/~nmatsuna/Site/papers/TetLett2010.pdf

Ravish K. Akhani

Papers

Linear free-energy relationship and rate study on a silylation-based kinetic resolution: mechanistic insights.

Diastereoselective and enantioselective silylation of 2-arylcyclohexanols.

Polystyrene-Supported Triphenylsilyl Chloride for the Silylation-Based Kinetic Resolution of Secondary Alcohols

Incorporation of hexafluorobutyne into furans or phenols via reaction with iron(0) carbene or vinylketene complexes.

Halogenated catechols from cycloaddition reactions of η4-(2-ethoxyvinylketene)iron(0) complexes with 1-haloalkynes