Re-Engineering Organocatalysts for Asymmetric Friedel–Crafts Alkylation of Indoles through Computational Studies

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

Enhancing the photocatalytic upcycling of polystyrene to benzoic acid: a combined computational-experimental approach for acridinium catalyst design

Converting polystyrene into value-added oxygenated aromatic compounds is an attractive end-of-life upcycling strategy. However, identification of appropriate catalysts often involves laborious and time-consuming empirical screening. Herein, after demonstrating the feasibility of using acridinium salts for upcycling polystyrene into benzoic acid by photoredox catalysis for the first time, we applied low-cost descriptor-based combinatorial in silico screening to predict the photocatalytic performance of a family of potential candidates. Through this approach, we identified a non-intuitive fluorinated acridinium catalyst that outperforms other candidates for converting polystyrene to benzoic acid in useful yields at low catalyst loadings (≤5 mol%). In addition, this catalyst also proved effective with real-life polystyrene waste containing dyes and additives. Our study underscores the potential of computer-aided catalyst design for valorizing polymeric waste into essential chemical feedstock for a more sustainable future.

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of 4000 experimentally derived organocatalysts along with their corresponding building blocks and combinatorially enriched structures. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available (https://archive.materialscloud.org/record/2022.106) and constitute the starting point to build generative and predictive models for organocatalyst performance.

Theoretical Perspectives in Organocatalysis

It is clear that the field of organocatalysis is continuously expanding during the last decades. With increasing computational capacity and new techniques, computational methods have provided a more economic approach to explore different chemical systems. This review offers a broad yet concise overview of current state-of-the-art studies that have employed novel strategies for catalyst design. The evolution of the all different theoretical approaches most commonly used within organocatalysis is discussed, from the traditional approach, manual-driven, to the most recent one, machine-driven.

Recent Advances in the Catalytic Asymmetric Friedel-Crafts Reactions of Indoles

Functionalized chiral indole derivatives are privileged and versatile organic frameworks encountered in numerous pharmaceutically active agents and biologically active natural products. The catalytic asymmetric Friedel-Crafts reaction of indoles, catalyzed by chiral metal complexes or chiral organocatalysts, is one of the most powerful and atom-economical approaches to access optically active indole derivatives. Consequently, a wide range of electrophilic partners including α,β-unsaturated ketones, esters, amides, imines, β,γ-unsaturated α-keto- and α-ketiminoesters, ketimines, nitroalkenes, and many others have been successfully employed to achieve a plethora of functionalized chiral indole moieties. In particular, strategies for C-H functionalization in the phenyl of indoles require incorporation of a directing or blocking group in the phenyl or azole ring of indole. The discovery of chiral catalysts which can control enantiodiscrimination has gained a great deal of attention in recent years. This review will provide an updated account on the application of the asymmetric Friedel-Crafts reaction of indoles in the synthesis of diverse chiral indole derivatives, covering the timeframe from 2011 to today.

Diaminomethylenemalononitrile as a Chiral Single Hydrogen Bond Catalyst: Application to Enantioselective Conjugate Addition of α-Branched Aldehydes

An improved diaminomethylenemalononitrile organocatalyst, bearing a N,N-disubstituted structure, promoted enantioselective conjugate addition reaction of α-branched aldehydes with vinyl sulfone, affording adducts with excellent enantioselectivities (up to 96% ee). Mechanistic studies revealed that the diaminomethylenemalononitrile motif holds the vinyl sulfone substrate using a single hydrogen bond accompanied by multiple weak interactions, including electrostatic C-H⋅⋅⋅O interactions.

Trends in computational molecular catalyst design

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.

Emerging computational approaches for the study of regio- and stereoselectivity in organic synthesis

Computational chemistry has become important in organic synthesis as it provides a detailed understanding of molecular structures and properties and detailed reaction mechanisms.