A Holistic Approach to Determining Stereochemistry of Potential Pharmaceuticals by Circular Dichroism with β-Lactams as Test Cases

This paper’s main objective is to show that many different factors must be considered when solving stereochemical problems to avoid misleading conclusions and obtain conclusive results from the analysis of spectroscopic properties. Particularly in determining the absolute configuration, the use of chiroptical methods is crucial, especially when other techniques, including X-ray crystallography, fail, are not applicable, or give inconclusive results. Based on various β-lactam derivatives as models, we show how to reliably determine their absolute configuration (AC) and preferred conformation from circular dichroism (CD) spectra. Comprehensive CD analysis, employing both approaches, i.e., traditional with their sector and helicity rules, and state-of-the-art supported by quantum chemistry (QC) calculations along with solvation models for both electronic (ECD) and vibrational (VCD) circular dichroism ranges, allows confident defining stereochemistry of the b-lactams studied. Based on an in-depth analysis of the results, we have shown that choosing a proper chiroptical method/s strictly depends on the specific case and certain structural features.

Recent Developments of Computational Methods for pKa Prediction Based on Electronic Structure Theory with Solvation Models

The protonation/deprotonation reaction is one of the most fundamental processes in solutions and biological systems. Compounds with dissociative functional groups change their charge states by protonation/deprotonation. This change not only significantly alters the physical properties of a compound itself, but also has a profound effect on the surrounding molecules. In this paper, we review our recent developments of the methods for predicting the Ka, the equilibrium constant for protonation reactions or acid dissociation reactions. The pKa, which is a logarithm of Ka, is proportional to the reaction Gibbs energy of the protonation reaction, and the reaction free energy can be determined by electronic structure calculations with solvation models. The charge of the compound changes before and after protonation; therefore, the solvent effect plays an important role in determining the reaction Gibbs energy. Here, we review two solvation models: the continuum model, and the integral equation theory of molecular liquids. Furthermore, the reaction Gibbs energy calculations for the protonation reactions require special attention to the handling of dissociated protons. An efficient method for handling the free energy of dissociated protons will also be reviewed.

Implicitly perturbed Hamiltonian: a class of versatile and general-purpose molecular representations for machine learning

Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures to quantitative representations as suitable machine-learning inputs play a central role. Many different molecular representations and the state-of-the-art ones, although efficient in studying numerous molecular features, still are suboptimal in many challenging cases, as discussed in the context of the present research. The main aim of the present study is to introduce the Implicitly Perturbed Hamiltonian (ImPerHam) as a class of versatile representations for more efficient machine learning of challenging problems in molecular sciences. ImPerHam representations are defined as energy attributes of the molecular Hamiltonian, implicitly perturbed by a number of hypothetic or real arbitrary solvents based on continuum solvation models. We demonstrate the outstanding performance of machine-learning models based on ImPerHam representations for three diverse and challenging cases of predicting inhibition of the CYP450 enzyme, high precision, and transferrable evaluation of conformational energy of molecular systems, and accurately reproducing solvation free energies for large benchmark sets.

Benchmarking Free Energy Calculations in Liquid Aliphatic Ketone Solvents Using the 3D-RISM-KH Molecular Solvation Theory

The three-dimensional reference interaction site model of the molecular solvation theory with the Kovalenko–Hirata closure is used to calculate the free energy of solvation of organic solutes in liquid aliphatic ketones. The ketone solvent sites were modeled using a modified united-atom force field. The successful application of these solvation models in calculating ketone–water partition coefficients of a large number of solutes supports the validation and benchmarking reported here.

Viscosity Investigations on the Binary Systems of (1 ChCl:2 Ethylene Glycol) DES and Methanol or Ethanol

In this study, the viscosity behavior of two mixtures of Ethaline (1 ChCl:2 ethylene glycol) with either methanol or ethanol were investigated over the temperature range of 283.15–333.15 K at atmospheric pressure. The measured viscosities of neat Ethaline, methanol, and ethanol showed reliable agreement with the corresponding reported literature values. The mixture viscosities were modeled by an Arrhenius-like model to determine the behavior of viscosity with respect to temperature. The data were also modeled by the four well-known mixture viscosity models of Grunberg–Nissan, Jouyban–Acree, McAllister, and Preferential Solvation. All of the model results were reliable, with the Jouyban–Acree and Preferential Solvation models showing the most accurate agreement with the experimental measurements. The Jones–Dole viscosity model was also investigated for the measured viscosities, and by analyzing the results of this model, strong interactions among Ethaline and the alcohol molecules were proposed for both systems. As a final analysis, viscosity deviations of the investigated systems were calculated to study the deviations of the viscosity behaviors with respect to ideal behavior. Both systems showed negative viscosity deviations at all of the investigated temperatures, with the negative values tending towards zero, and hence more ideal behavior, with increasing temperatures. Moreover, in order to correlate the calculated viscosity deviations, the Redlich–Kister model was successfully used for both systems and at each investigated temperature.

Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model

Theoretical estimation of solvation free energy by continuum solvation models, as a standard approach in computational chemistry, is extensively applied by a broad range of scientific disciplines. Nevertheless, the current widely accepted solvation models are either inaccurate in reproducing experimentally determined solvation free energies or require a number of macroscopic observables which are not always readily available. In the present study, we develop and introduce the Machine-Learning Polarizable Continuum solvation Model (ML-PCM) for a substantial improvement of the predictability of solvation free energy. The performance and reliability of the developed models are validated through a rigorous and demanding validation procedure. The ML-PCM models developed in the present study improve the accuracy of widely accepted continuum solvation models by almost one order of magnitude with almost no additional computational costs. A freely available software is developed and provided for a straightforward implementation of the new approach.