QSAR analysis of the catalytic asymmetric ethylation of ketone using physical steric parameters of chiral ligand substituents

Enantioselectivity-Evaluation of Chiral Copper(II) Complexes Coordinated by Novel Chiral Tetradentate Ligands for Free Amino Acids by Mass Spectrometry Coupled With the Isotopically Labeled Enantiomer Method

A series of copper(II) complexes with chiral tetradentate ligands, N,N′-ethylene- bis(S-amino acid methyl amide or methyl ester) prepared from S-alanine, S-phenylalanine, S-valine or S-proline, was generated in methanol. The copper complexes provided three component complexes in the presence of a free chiral amino acid. The enantioselectivity for the amino acid was evaluated by electrospray ionization-mass spectrometry coupled with the deuterium-labeled enantiomer method and these copper complexes were found to exhibit high enantioselectivity for free amino acids having bulky side chains. This result suggests that steric interaction between the tetradentate ligand and free amino acid was a major factor in chiral recognition. The copper complex with a chiral tetradentate ligand prepared from S-proline showed opposite enantioselectivity to copper complexes consisting of tetradentate ligands prepared from other S-amino acids. The conformational difference of the tetradentate ligand in the copper complex was found to be significant for enantioselectivity.

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.

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.

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.