Correlating the Effects of the N-Substituent Sizes of Chiral 1,2-Amino Phosphinamide Ligands on Enantioselectivities in Catalytic Asymmetric Henry Reaction Using Physical Steric Parameters

In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H2

Cheminformatics-based machine learning (ML) has been employed to determine optimal reaction conditions, including catalyst structures, in the field of synthetic chemistry. However, such ML-focused strategies have remained largely unexplored in the context of catalytic molecular transformations using Lewis-acidic main-group elements, probably due to the absence of a candidate library and effective guidelines (parameters) for the prediction of the activity of main-group elements. Here, the construction of a triarylborane library and its application to an ML-assisted approach for the catalytic reductive alkylation of aniline-derived amino acids and C-terminal-protected peptides with aldehydes and H2 is reported. A combined theoretical and experimental approach identified the optimal borane, i.e., B(2,3,5,6-Cl4-C6H)(2,6-F2-3,5-(CF3)2-C6H)2, which exhibits remarkable functional-group compatibility toward aniline derivatives in the presence of 4-methyltetrahydropyran. The present catalytic system generates H2O as the sole byproduct.

Recent advances in the synthesis and applications of phosphoramides

Phosphoramide, as an important framework of many biologically active molecules, has attracted widespread attention in recent decades. It is not only widely used in pharmaceuticals because of its excellent biological activities, but it also shows good performance in organic dyes, flame retardants and extractors. Thus, it is of great significance to develop effective and convenient methods for the synthesis of phosphoramides. In this review, the recent advancements made in the synthesis routes and applications of phosphoramides are discussed. The synthetic strategies of phosphoramides can be separated into five categories: phosphorus halides as the substrate, phosphates as the substrate, phosphorus hydrogen as the substrate, azides as the substrate and other methods. The latest examples of these methods are provided and some representative mechanisms are also described.

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

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.

MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry

AIMS

Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.).

BACKGROUND

Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.).

OBJECTIVE

Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures.

METHODS

We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied.

RESULTS

We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series.

CONCLUSION

The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.

Photothermal conversion triggered thermal asymmetric catalysis within metal nanoparticles loaded homochiral covalent organic framework

For seeking high enantiopurity, the previously reported thermal asymmetric catalysis is usually carried out at low temperature sometimes with limited yield, that is, the high enantiomeric excess (ee) usually at the cost of high yield. Thus, the achieving both high stereoselectivity and yield is an enormous challenge. We report herein two metal nanoparticle (M NP)-loaded and porphyrin-containing homochiral covalent organic framework (CCOF)-based composite catalysts, and their application in the thermally-driven asymmetric one-pot Henry and A³-coupling reactions. All the reactions are conducted at elevated temperatures with both excellent stereoselectivity and yield which resulted from the synergy of CCOF confinement effect and M NP catalytic activation. Notably, the needed thermal energy for the asymmetric reactions herein is derived from the photothermal conversion via porphyrin-based CCOF upon irradiation with visible light. Remarkably, the CCOF confinement effect can be effectively maintained up to 100 °C for the asymmetric one-pot Henry and A³-coupling reactions herein.

Achieving both high stereoselectivity and yield is a challenge for conventional asymmetric catalysis. Here, the authors report two metal nanoparticle-loaded and porphyrin-containing homochiral covalent organic framework-based composite catalysts that exhibit high stereoselectivity and yield in the thermally-driven asymmetric reactions.

Predictive and mechanistic multivariate linear regression models for reaction development

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis.

Multidimensional Correlations in Asymmetric Catalysis through Parameterization of Uncatalyzed Transition States

The study of the oxidative amination of tetrahydroisoquinolines under chiral-anion phase-transfer (CAPT) catalysis by multidimensional correlation analysis (MCA) is revisited. The parameterization of the transition states (TSs) for the uncatalyzed reaction, the introduction of conformational descriptors, and the use of computed interaction energies and distances as parameters allowed access to a considerably simplified mathematical correlation of substrate and catalyst structure to enantioselectivity. The equation obtained is suggestive of key interactions occurring at the TS. Specifically, the CAPT catalyst is proposed to coordinate the intermediate iminium cation by P=O⋅⋅⋅H-O hydrogen-bonding and N⋅⋅⋅H-C electrostatic interactions. The conformational freedom of the benzyl substituent of the substrate was also found to be important in providing an efficient mode of molecular recognition.

Chiral Brønsted Acid-Catalyzed Enantioselective α-Amidoalkylation Reactions: A Joint Experimental and Predictive Study

Enamides with a free NH group have been evaluated as nucleophiles in chiral Brønsted acid-catalyzed enantioselective α-amidoalkylation reactions of bicyclic hydroxylactams for the generation of quaternary stereocenters. A quantitative structure-reactivity relationship (QSRR) method has been developed to find a useful tool to rationalize the enantioselectivity in this and related processes and to orient the catalyst choice. This correlative perturbation theory (PT)-QSRR approach has been used to predict the effect of the structure of the substrate, nucleophile, and catalyst, as well as the experimental conditions, on the enantioselectivity. In this way, trends to improve the experimental results could be found without engaging in a long-term empirical investigation.

Model Building Using Linear Free Energy Relationship Parameters-Eliminating Calibration Curves for Optical Analysis of Enantiomeric Excess

Linear free energy relationship (LFER) parameters are routinely used to parametrize physicochemical effects while investigating reaction mechanisms. In this Communication, we describe an alternate application for LFERs: training sets for model building in an analytical application. In this study, the sterics, quantified by Charton parameters (Δv), of nine secondary chiral alcohol analytes were correlated to the circular dichroism output from a chiral alcohol optical sensor. To test the validity of the model, the correlative linear model was applied to determine the enantiomeric excess of samples of two alcohols without a priori knowledge of a calibration curve. The error in this method was comparable to those of previous experimental methods (<5%).

Perturbation theory model of reactivity and enantioselectivity of palladium-catalyzed Heck–Heck cascade reactions

A new multi-output PT-QSRR model to correlate and predict the enantioselectivity and yield of Heck–Heck cascade reactions has been developed.

Novel synthesis of N-alkyl amines from tandem coupling of either methylamine or nitroalkane with aldehyde

A novel tandem method was developed to synthesize N-alkyl amines from coupling of either methylamine or nitroalkane with aldehyde.

Chemo- and Enantioselective Addition and β-Hydrogen Transfer Reduction of Carbonyl Compounds with Diethylzinc Reagent in One Pot Catalyzed by a Single Chiral Organometallic Catalyst

Using a single chiral phosphoramide-Zn(II) complex as the catalyst, the asymmetric β-H transfer reduction of aromatic α-trifluoromethyl ketones and enantioselective addition of aromatic aldehydes with Et2Zn in one pot were successfully realized, affording the corresponding additive products of secondary alcohols in high yields (up to 99%) with excellent enantioselectivities (up to 98% ee) and the reduction products of α-trifluoromethyl alcohols in good to excellent yields with up to 77% ee.

Reactivity-based dynamic covalent chemistry: reversible binding and chirality discrimination of monoalcohols

In an effort to develop reactivity-based dynamic covalent bonding and to expand the scope and application of the dynamic covalent chemistry, in situ-generated simple generic iminium ions were utilized for the dynamic covalent binding of monoalcohols with high affinity. Hammett analysis was conducted to manipulate the equilibrium and correlate with the reactivity of reactants. The structural features of aldehydes and secondary amines were identified, and both polar and steric effects have significant impact on the binding. In particular, the substrates which can participate in π-π and polar-π interactions are able to afford apparent equilibrium constants in the magnitude of 10(4) M(-2), demonstrating the power of weak supramolecular forces to stabilize the dynamic covalent assembly. The generality of the assembly was validated with a series of mono secondary alcohols. To showcase the practicality of our system, chirality discrimination and ee measurement of chiral secondary alcohols were achieved.

One-pot protocol to synthesize N-(β-nitro)amides by tandem Henry/Ritter reaction

A novel, efficient and atom economical one pot protocol for the synthesis of N-(β-nitro)amides has been described by combining the Henry reaction with the Ritter reaction.