Modern computational analysis has provided chemists the power to rapidly perform complex calculations, leading to increased understanding and expedited optimization of chemical systems. An important application of this technology is its implementation in catalyst design. Currently, intensive quantum mechanical calculations can be routinely performed to generate optimized geometries and energies of reactive intermediates and transition states. This information may then be used to guide chemical intuition, allowing experimentalists to make advances in the field at an unprecedented pace. However, despite the power of transition state modeling to aid in the design of new catalytic entities, the reliance on chemical intuition to do so is inherently flawed. No matter how experienced or insightful the practitioner, several problems conspire to hinder rapid progress: (1) the lack of detailed mechanistic understanding of the rate and stereodetermining events in a process, (2) the inherent limitations of the human brain to find patterns in large collections of data, and (3) the lack of quantitative measures to aid the choice of catalyst candidates. Moreover, current computational techniques work retrospectively to explain experimental observations. Because of the high computational demands of quantum mechanical calculations, it is not feasible to use these techniques predictively to guide catalyst design. Further, as mentioned above, the use of this approach requires a thorough mechanistic understanding of the transformation in question.

Given the limitations of ab initio computational methods, the organic chemistry community needs different computational methods that can be used predictively to select catalyst candidates from large libraries of in silico generated candidates to expedite the optimization of new processes. Our chemoinformatics approach provides an attractive alternative because: (1) no mechanistic information is needed as the substrates are not included in the analysis (2) catalyst structures are characterized by 3D-descriptors that quantify the steric and electronic properties of thousands of candidate molecules and (3) the suitability of a given candidate can be quantified by comparing its properties to a computationally derived model based on experimental data. This kind of analysis, known as quantitative structure activity relationships (QSAR), is popular in the pharmaceutical industry for understanding the activity therapeutically relevant molecules. The application of QSAR to catalysis is conceptually similar except that the correlation of molecular properties is made to a chemical transformation rather than a binding event or inhibition of a biological process.

The implementation of a chemoinformatic optimization involves a series of interdependent computational and experimental phases. First, a library of potential catalyst structures is constructed in silico based on a particular, diversifiable molecular scaffold. Next, certain properties, termed descriptors, of the molecules in the in silico library are calculated, and a small subset of the candidates is systematically selected that represent the greatest chemical diversity in the library. The candidates in this test set are synthesized, evaluated in a given transformation, and the empirical data of interest (rate, er, dr, TOF, TON) is recorded. Next, using a number of different regression algorithms, a correlation of some combination of molecular descriptors and the empirical data is sought and then validated for statistical significance. Finally, the mathematical correlation is then applied to the remainder of the in silico library to identify catalyst candidates that are predicted to be superior for the given transformation.

Our initial foray into the application of Chemoinformatic optimization of catalytic reactions focused on phase transfer catalysis (PTC). PTC is an extremely useful method for performing nearly any type of reaction involving an ionic starting material or intermediate. A number of characteristics of PTC reactions make them especially attractive for industrial applications including ease of scalability, their intrinsic “green nature”, and the ability to catalyze a wide variety of reaction types, ranging from redox processes to many carbon-carbon-bond forming reactions. It is estimated that more than 500 commercial PTC processes are now being performed using at least 25 million pounds of catalyst per year! The sales of products manufactured by processes consisting of at least one major PTC step were at least $10 billion/year ($5 billion in polymers, $3 billion in pharmaceuticals, $2 billion in agrochemicals and $1 billion in flavors, fragrances, dyes, surfactants, et al.).

PTC has already proven to be a broadly applicable and general method to perform strong base chemistry in a catalytic, asymmetric manner. A number of modified cinchona alkaloids as well as synthetic, quaternary ammonium salts have been introduced for, inter alia: (1) single and double alkylation of glycine imines, (2) ketone alkylations, (3) Michael additions, (4) aldol, Mannich and Darzens reactions, (5) epoxidations, and (6) aziridinations.

We have initiated a long-term program aimed at elucidating the physical organic foundations of APTC employing a chemoinformatic analysis of phase transfer reactions with libraries of enantiomerically enriched quaternary ammonium ions. The synthesis of the quaternary ammonium ions follows a diversity oriented approach wherein our tandem inter[4+2]/intra[3+2] cycloaddition of nitroalkenes serves as the key transformation. A two part synthetic strategy comprised of: (1) preparation of enantioenriched scaffolds and (2) development of parallel synthesis procedures is described. The strategy allows for the facile introduction of four variable groups in the vicinity of a stereogenic quaternary ammonium ion. The quaternary ammonium ions exhibited a wide range of activity and to a lesser degree enantioselectivity. Catalyst activity and selectivity are rationalized in a qualitative way based on the effective positive potential of the ammonium ion.

Read the most recent review: 328, 329, 358, 362

Catalyst Library Synthesis using the Tandem Inter[4+2]/Intra [3+2] Cycloaddition


Steric and Electrostatic Contour Maps from CoMFA Analysis of Enantioselectivity



Cross-Sectional Area Depictions of Various Catalysis that Correlate with Activity


Modification of Cinchona Alkaloid Skeleton