Monika Pareek

Bio: Monika Pareek is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Enantioselective synthesis & Catalysis. The author has an hindex of 4, co-authored 6 publications receiving 97 citations.

Tale of the Breslow intermediate, a central player in N-heterocyclic carbene organocatalysis: then and now.

Abstract: N-Heterocyclic carbenes (NHCs) belong to the popular family of organocatalysts used in a wide range of reactions, including that for the synthesis of complex natural products and biologically active compounds. In their organocatalytic manifestation, NHCs are known to impart umpolung reactivity to aldehydes and ketones, which are then exploited in the generation of homoenolate, acyl anion, and enolate equivalents suitable for a plethora of reactions such as annulation, benzoin, Stetter, Claisen rearrangement, cycloaddition, and C–C and C–H bond functionalization reactions and so on. A common thread that runs through these NHC catalyzed reactions is the proposed involvement of an enaminol, also known as the Breslow intermediate, formed by the nucleophilic addition of an NHC to a carbonyl group of a suitable electrophile. In the emerging years of NHC catalysis, enaminol remained elusive and was largely considered a putative intermediate owing to the difficulties encountered in its isolation and characterization. However, in the last decade, synergistic efforts utilizing an array of computational and experimental techniques have helped in gaining important insights into the formation and characterization of Breslow intermediates. Computational studies have suggested that a direct 1,2-proton transfer within the initial zwitterionic intermediate, generated by the action of an NHC on the carbonyl carbon, is energetically prohibitive and hence the participation of other species capable of promoting an assisted proton transfer is more likely. The proton transfer assisted by additives (such as acids, bases, other species, or even a solvent) was found to ease the kinetics of formation of Breslow intermediates. These important details on the formation, in situ detection, isolation, and characterization of the Breslow intermediate are scattered over a series of reports spanning well over a decade, and we intend to consolidate them in this review and provide a critical assessment of these developments. Given the central role of the Breslow intermediate in organocatalytic reactions, this treatise is expected to serve as a valuable source of knowledge on the same.

Cooperative Asymmetric Catalysis by N-Heterocyclic Carbenes and Brønsted Acid in γ-Lactam Formation: Insights into Mechanism and Stereoselectivity

Abstract: Current developments in the burgeoning area of cooperative asymmetric catalysis indicate the use of N-heterocyclic carbenes (NHCs) in conjunction with other catalysts such as a Bronsted acid. Herein, mechanistic insights derived through a comprehensive DFT (M06-2X) computational study on a dual catalytic reaction between an enal and an imine leading to trans-γ-lactams, catalyzed by a chiral NHC and benzoic acid, is presented. In the most preferred pathway, we note that the NHC catalyst activates one of the reactants (enal) in the form of a Breslow intermediate, whereas the electrophilic partner (imine) is activated by the benzoic acid through protonation of the imino nitrogen. In this article, we focus on the origin of cooperative action of both catalysts as well as on the stereoselectivity by identifying the stereocontrolling transition states. The explicit and cooperative participation of the Bronsted acid and NHC lowers the energetic barrier both in the Breslow intermediate formation and in the stereoc...

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation.

Abstract: Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate-catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%ee) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations.

An iron(ii)-based metalloradical system for intramolecular amination of C(sp2)–H and C(sp3)–H bonds: synthetic applications and mechanistic studies

Abstract: A catalytic system for intramolecular C(sp2)–H and C(sp3)–H amination of substituted tetrazolopyridines has been successfully developed. The amination reactions are developed using an iron-porphyrin based catalytic system. It has been demonstrated that the same iron-porphyrin based catalytic system efficiently activates both the C(sp2)–H and C(sp3)–H bonds of the tetrazole as well as azide-featuring substrates with a high level of regioselectivity. The method exhibited an excellent functional group tolerance. The method affords three different classes of high-value N-heterocyclic scaffolds. A number of important late-stage C–H aminations have been performed to access important classes of molecules. Detailed studies (experimental and computational) showed that both the C(sp2)–H and C(sp3)–H amination reactions involve a metalloradical activation mechanism, which is different from the previously reported electro-cyclization mechanism. Collectively, this study reports the discovery of a new class of metalloradical activation modes using a base metal catalyst that should find wide application in the context of medicinal chemistry, drug discovery and industrial applications.

A review on extreme learning machine

Abstract: Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance In this paper, we hope to present a comprehensive review on ELM Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram The controversies of ELM were also discussed in this paper We aim to report these advances and find some future perspectives

Recent Advances on Computational Investigations of N-Heterocyclic Carbene Catalyzed Cycloaddition/Annulation Reactions: Mechanism and Origin of Selectivities

Abstract: The developments of theoretical studies on NHC-catalyzed [n + 2] (n = 2, 3, 4) and other cycloaddition/annulation reactions have been summarized in this review. The detailed mechanisms, role of NHC, and origin of chemo- and stereo-selectivity of this kind of reactions were illustrated to provide valuable insights for rational design of new NHC-catalyzed cycloaddition/annulation reactions with high selectivities. Moreover, computational and theoretical methods commonly used in the researches were also mentioned to open the door for deeply exploring the general principle of NHC-catalyzed reactions in theory.

Origin of Stereoselectivity in Cooperative Asymmetric Catalysis Involving N-Heterocyclic Carbenes and Lewis Acids toward the Synthesis of Spirooxindole Lactone

Abstract: An increasing number of examples are now being reported that use chiral N-heterocyclic carbenes (NHCs) in conjunction with Lewis acids to enhance catalytic potential. Herein, we provide molecular insights into an NHC-catalyzed stereoselective annulation reaction between N-methylisatin and an enal leading to spirooxindole lactone in the presence of LiCl as the Lewis acid. Mechanistic features as well as the origin of enantio- and diastereoselectivities of the catalytic reaction have been unraveled using the density functional theory (B3LYP-D3) method. The key mechanistic steps of the reaction are identified to proceed through the formation of a Breslow intermediate between the chiral NHC catalyst and the enal, an enantioselective addition of the re face of this intermediate to the re face of the carbonyl group of N-methylisatin, and an intramolecular proton transfer and lactonization that eventually provide access to (2S,3R)-spirooxindole lactone as the final product. In the most preferred pathway, the Lew...

Machine learning for advanced energy materials

Abstract: The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low success probabilities, high time consumption, and high computational cost associated with the traditional methods of developing energy materials. Following this, new research concepts and technologies to promote the research and development of energy materials become necessary. The latest advancements in artificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of energy materials. Furthermore, the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment. In this article, the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented. A comprehensive introduction to the fundamentals of machine learning is also provided, including open-source databases, feature engineering, machine learning algorithms, and analysis of machine learning model. Afterwards, the latest progress in data-driven materials science and engineering, including alkaline ion battery materials, photovoltaic materials, catalytic materials, and carbon dioxide capture materials, is discussed. Finally, relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.