Homology Modeling and Molecular Dynamics-Driven Search for Natural Inhibitors That Universally Target Receptor-Binding Domain of Spike Glycoprotein in SARS-CoV-2 Variants.

The rapid spread of SARS-CoV-2 required immediate actions to control the transmission of the virus and minimize its impact on humanity. An extensive mutation rate of this viral genome contributes to the virus' ability to quickly adapt to environmental changes, impacts transmissibility and antigenicity, and may facilitate immune escape. Therefore, it is of great interest for researchers working in vaccine development and drug design to consider the impact of mutations on virus-drug interactions.

Zeta potentials (ζ) of metal oxide nanoparticles: A meta-analysis of experimental data and a predictive neural networks modeling.

Zeta potential is usually measured to estimate the surface charge and the stability of nanomaterials, as changes in these characteristics directly influence the biological activity of a given nanoparticle. Nowadays, theoretical methods are commonly used for a pre-screening safety assessments of nanomaterials. At the same time, the consistency of data on zeta potential measurements in the context of environmental impact is an important challenge.

Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions.

The production of nanomaterials continues its rapid growth; however, newly manufactured nanomaterials' environmental and health safety are among the most significant concerns. A safety assessment is usually a lengthy and costly process, so computational studies are often used to complement experimental testing. One of the most time-efficient techniques is structure-activity relationships (SAR) modeling.

Predictive Modeling of Critical Temperatures in Superconducting Materials.

In this study, we have investigated quantitative relationships between critical temperatures of superconductive inorganic materials and the basic physicochemical attributes of these materials (also called quantitative structure-property relationships). We demonstrated that one of the most recent studies (titled "A data-driven statistical model for predicting the critical temperature of a superconductor" and published in Computational Materials Science by K. Hamidieh in 2018) reports on models that were based on the dataset that contains 27% of duplicate entries.

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.