QSPR modeling of optical rotation of amino acids using specific quantum chemical descriptors

Simplex representation of molecular structure as universal QSAR/QSPR tool

We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.

Machine Learning Classification of One-Chiral-Center Organic Molecules According to Optical Rotation

In this study, machine learning algorithms were investigated for the classification of organic molecules with one carbon chiral center according to the sign of optical rotation. Diverse heterogeneous data sets comprising up to 13,080 compounds and their corresponding optical rotation were retrieved from Reaxys and processed independently for three solvents: dichloromethane, chloroform, and methanol. The molecular structures were represented by chiral descriptors based on the physicochemical and topological properties of ligands attached to the chiral center. The sign of optical rotation was predicted by random forests (RF) and artificial neural networks for independent test sets with an accuracy of up to 75% for dichloromethane, 82% for chloroform, and 82% for methanol. RF probabilities and the availability of structures in the training set with the same spheres of atom types around the chiral center defined applicability domains in which the accuracy is higher.

N-arylnaphthylamines as inhibitors of human immunodeficiency virus integrase - lens epithelium-derived growth factor interactions: theoretical studies

Presented work reports a comprehensive theoretical study on the inhibitory nature of N-arylnaphthylamines in Human Immunodeficiency Virus Integrase (HIV IN) - Lens Epithelium-Derived Growth Factor (LEDGF/p75) complexes. Factors influencing the inhibition efficiency in AlphaScreen% assay are evaluated and explained through the structure- and ligand-based studies; including molecular docking, molecular dynamics calculations, and quantitative structure-activity relationship (QSAR) approach. It has been shown that N-arylnaphthylamines possess a wide variety of binding poses. Three QSAR models have been developed using structural descriptors and descriptors derived from docking calculations. The activity of untested N-arylnaphthylamines have been predicted using the most successful model. Proposed here technique could become a useful tool for ligand selection, accelerating the development of a new generation of anti-HIV medications. [Formula: see text] Communicated by Ramaswamy H. Sarma.