High-Throughput Strategies for the Discovery of Catalysts

Abstract

IntroductionThe major application of combinatorial chemistry remains the search for biologically active molecules.[1] Diversity-based strategies, however, might be effective in the identification of compounds that have attractive properties. Combinatorial and related strategies have indeed been utilized recently in investigations involving materials science, [2] molecular recognition, [3,4] polymer chemistry, [5] and asymmetric catalysis. [6] This article is a brief overview of the recently developed diversity-based approaches in the screening and identification of effective metal-based catalysts for enantioselective synthesis. In a few instances, it is likely that the more traditional design and screening approaches, often based on a priori mechanistic bias, would have been less successful, at least within the same time span. The searches for therapeutic agents and asymmetric catalysts share a number of facets. Traditionally, both fields have relied on iterative approaches wherein a single compound is designed, synthesized, and tested (Scheme 1). The cycle is repeated until a compound is obtained with the desired levels of enantioselectivity or activity. In contrast, as illustrated in Scheme 1, a high-throughput strategy enables one to generate and test simultaneously considerably larger numbers of candidates, potentially reducing the entire search cycle to one or two iterations. Design Synthesis Testing Design Synthesis Testing ClassicalHigh Throughput Scheme 1. The diversity-based approach can provide a wealth of data on reactivity and selectivity in an efficient manner.Combinatorial chemistry brings together rational design and high-throughput evaluation; it is rooted in empirical observations and logical deduction. Structure ± selectivity observations remain the basis for propagating molecular features from one generation of catalysts to the next. Such strategies therefore permit more initial guesses and a greater allowance for failure. Combinatorial chemistry is particularly well-suited to optimizing novel and previously unexamined reactions for which little mechanistic data is available. Such strategies can be viewed as the chemists attempt to address the notion that mechanistic subtleties that often differentiate the selectivity and reactivity of one substrate or catalyst from another may not be generalized. Such a broad-based approach relieves the chemist of the risk of following a relatively narrow path selected on the basis of fickle mechanistic parameters. It is perhaps fair to state that the development of almost all successful asymmetric catalysts has benefited, at some point, from serendipitous observations. Combinatorial chemistry integrates this aspect of catalyst discovery into the overall search process, increasing the rate at which advantageous mutations can occur. Nevertheless, a combinatorial approach can significantly promote the mechanistic studies of new asymmetric processes, as it can put forth a large structure ± selectivity database from which mechanistic paradigms can be generated. Hi...

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