Calculation of VS,max and Its Use as a Descriptor for the Theoretical Calculation of pKa Values for Carboxylic Acids

N-Triflylphosphoramides: highly acidic catalysts for asymmetric transformations

N-Triflylphosphoramides (NTPA), have become increasingly popular catalysts in the development of enantioselective transformations as they are stronger Brønsted acids than the corresponding phosphoric acids (PA). Their highly acidic, asymmetric active site can activate difficult, unreactive substrates. In this review, we present an account of asymmetric transformations using this type of catalyst that have been reported in the past ten years and we classify these reactions using the enantio-determining step as the key criterion. This compendium of NTPA-catalysed reactions is organised into the following categories: (1) cycloadditions, (2) electrocyclisations, polyene and related cyclisations, (3) addition reactions to imines, (4) electrophilic aromatic substitutions, (5) addition reactions to carbocations, (6) aldol and related reactions, (7) addition reactions to double bonds, and (8) rearrangements and desymmetrisations. We highlight the use of NTPA in total synthesis and suggest mnemonics which account for their enantioselectivity.

Using Atomic Charges to Describe the pKa of Carboxylic Acids

In this study, we present an accurate protocol for the fast prediction of pK_a's of carboxylic acids based on the linear relationship between computed atomic charges of the anionic form of the carboxylate fragment and their experimental pK_a values. Five charge descriptors, three charge models, three solvent models, gas-phase calculations, several DFT methods (a combination of eight DFT functionals and fifteen basis sets), and four different semiempirical approaches were tested. Among those, the best combination to reproduce experimental pK_a's is to compute the natural population analysis atomic charge using the solvation model based on density model at the M06L/6-311G(d,p) level of theory and selecting the maximum atomic charge on the carboxylic oxygen atoms (R² = 0.955). The applicability of the suggested protocol and its stability along geometrical changes are verified by molecular dynamics simulations performed for a set of aspartate, glutamate, and alanine peptides. By reporting the calculated atomic charge of the carboxylate form into the linear relationship derived in this work, it should be possible to accurately estimate the amino acid's pK_a's in a protein environment.

Understanding the Chloride Affinity of Barbiturates for Anion Receptor Design

Due to their potential binding sites, barbituric acid (BA) and its derivatives have been used in metal coordination chemistry. Yet their abilities to recognize anions remain unexplored. In this work, we were able to identify four structural features of barbiturates that are responsible for a certain anion affinity. The set of coordination interactions can be finely tuned with covalent decorations at the methylene group. DFT-D computations at the BLYP-D3(BJ)/aug-cc-pVDZ level of theory show that the C-H bond is as effective as the N-H bond to coordinate chloride. An analysis of the electron charge density at the C-H⋅⋅⋅Cl^- and N-H⋅⋅⋅Cl^- bond critical points elucidates their similarities in covalent character. Our results reveal that the special acidity of the C-H bond shows up when the methylene group moves out of the ring plane and it is mainly governed by the orbital interaction energy. The amide and carboxyl groups are the best choices to coordinate the ion when they act together with the C-H bond. We finally show how can we use this information to rationally improve the recognition capability of a small cage-like complex that is able to coordinate NaCl.

Partitioning Pattern of Natural Products Based on Molecular Properties Descriptors Representing Drug-Likeness

A cheminformatics procedure for a partitioning model based on 135 natural compounds including Flavonoids, Saponins, Alkaloids, Terpenes and Triterpenes with drug-like features based on a descriptors pool was developed. The knowledge about the applicability of natural products as a unique source for the development of new candidates towards deadly infectious disease is a contemporary challenge for drug discovery. We propose a partitioning scheme for unveiling drug-likeness candidates with properties that are important for a prompt and efficient drug discovery process. In the present study, the vantage point is about the matching of descriptors to build the partitioning model applied to natural compounds with diversity in structures and complexity of action towards the severe diseases, as the actual SARS-CoV-2 virus. In the times of the de novo design techniques, such tools based on a chemometric and symmetrical effect by the implied descriptors represent another noticeable sign for the power and level of the descriptors applicability in drug discovery in establishing activity and target prediction pipeline for unknown drugs properties.

Accurate Estimation of pKb Values for Amino Groups from Surface Electrostatic Potential (VS,min) Calculations: The Isoelectric Points of Amino Acids as a Case Study

Theoretical calculation of equilibrium dissociation constants is a very computationally demanding and time-consuming process since it requires an extremely accurate computation of the solvation free energy changes for each of the species involved. By correlating the minimum surface electrostatic potential (V_S,min) on the nitrogen atom of several aliphatic amino groups-calculated at the density functional theory (DFT) ωB97X-D/cc-pVDZ level of theory-we obtained regression models for each kind of substitution pattern from which we interpolate their corresponding pK_b values with remarkable accuracy: primary R² = 0.9519; secondary R² = 0.9112; and tertiary R² = 0.8172 (N = 20 for each family). These models were validated with tests sets (N = 5) with mean absolute error (MAE) values of 0.1213 (primary), 0.4407 (secondary), and 0.3057 (tertiary). Combining this ansatz with another previously reported by our group to estimate pK_a values [Caballero-García, G.; et al. Molecules 2019, 24(1), 79] we are able to reproduce the isoelectric points of 13 amino acids with no titrable side chains with MAE = 0.4636 pI units.

Practical High-Quality Electrostatic Potential Surfaces for Drug Discovery Using a Graph-Convolutional Deep Neural Network

Inspecting protein and ligand electrostatic potential (ESP) surfaces in order to optimize electrostatic complementarity is a key activity in drug design. These ESP surfaces need to reflect the true electrostatic nature of the molecules, which typically means time-consuming high-level quantum mechanics (QM) calculations are required. For interactive design much faster alternative methods are required. Here, we present a graph convolutional deep neural network (DNN) model, trained on ESP surfaces derived from high quality QM calculations, that generates ESP surfaces for ligands in a fraction of a second. Additionally, we describe a method for constructing fast QM-trained ESP surfaces for proteins. We show that the DNN model generates ESP surfaces that are in good agreement with QM and that the ESP values correlate well with experimental properties relevant to medicinal chemistry. We believe that these high-quality, interactive ESP surfaces form a powerful tool for driving drug discovery programs forward. The trained model and associated code are available from https://github.com/AstexUK/ESP_DNN.

Calculating the Partition Coefficients of Organic Solvents in Octanol/Water and Octanol/Air

Partition coefficients define how a solute is distributed between two immiscible phases at equilibrium. The experimental estimation of partition coefficients in a complex system can be an expensive, difficult, and time-consuming process. Here a computational strategy to predict the distributions of a set of solutes in two relevant phase equilibria is presented. The octanol/water and octanol/air partition coefficients are predicted for a group of polar solvents using density functional theory (DFT) calculations in combination with a solvation model based on density (SMD) and are in excellent agreement with experimental data. Thus, the use of quantum-chemical calculations to predict partition coefficients from free energies should be a valuable alternative for unknown solvents. The obtained results indicate that the SMD continuum model in conjunction with any of the three DFT functionals (B3LYP, M06-2X, and M11) agrees with the observed experimental values. The highest correlation to experimental data for the octanol/water partition coefficients was reached by the M11 functional; for the octanol/air partition coefficient, the M06-2X functional yielded the best performance. To the best of our knowledge, this is the first computational approach for the prediction of octanol/air partition coefficients by DFT calculations, which has remarkable accuracy and precision.