QSTR Modeling to Find Relevant DFT Descriptors Related to the Toxicity of Carbamates.

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure-toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model. A structure-based evaluation by docking from a series of carbamates with acetylcholinesterase (AChE) was carried out. The toxicity of carbamates was predicted using physicochemical, structural, and quantum molecular descriptors employing a DFT approach. A statistical treatment was developed; the QSRT model showed a determination coefficient (R2) and a leave-one-out coefficient (Q2LOO) of 0.6584 and 0.6289, respectively.

catena-Poly[[[di-aqua-bis-[1,2-bis-(pyridin-4-yl)diazene]copper(II)]-µ-1,2-bis-(pyridin-4-yl)diazene] bis-(perchlorate)].

In the title compound, {[Cu(C10H8N4)3(H2O)2](ClO4)2} n , the coordination environment of the cationic Cu(II) atom is distorted octa-hedral, formed by pairs of symmetry-equivalent 1,2-bis-(pyridin-4-yl)diazene ligands, bridging 1,2-bis-(pyridin-4-yl)diazene ligands and two non-equivalent water mol-ecules. The 1,2-bis-(pyridin-4-yl)diazene mol-ecules form polymeric chains parallel to [-101] via azo bonds which are situated about inversion centres. Since the Cu(II) atom is situated on a twofold rotation axis, the monomeric unit has point symmetry 2. The perchlorate anions are disordered in a 0.536 (9):0.464 (9) ratio and are acceptors of water H atoms in medium-strong O-H⋯O hydrogen bonds with graph set R 4 (4)(12). The water mol-ecules, which are coordinated to the Cu(II) atom and are hydrogen-bonded to the perchlorate anions, form columns parallel to [010]. A π-π inter-action [centroid-centroid distance = 3.913 (2) Å] occurs between pyridine rings, and weak C-H⋯O inter-actions also occur.

Conceptual DFT, machine learning and molecular docking as tools for predicting LD50 toxicity of organothiophosphates.

CONTEXT: Several descriptors from conceptual density functional theory (cDFT) and the quantum theory of atoms in molecules (QTAIM) were utilized in Random Forest (RF), LASSO, Ridge, Elastic Net (EN), and Support Vector Machines (SVM) methods to predict the toxicity (LD50) of sixty-two organothiophosphate compounds. The A-RF-G1 and A-RF-G2 models were obtained using the RF method, yielding statistically significant parameters with good performance, as indicated by R2 values for the training set (R2Train) and R2 values for the test set (R2Test), around 0.90. METHODS: The molecular structure of all organothiophosphates was optimized via the range-separated hybrid functional ωB97XD with the 6-311 + + G** basis set. Seven hundred and eighty-seven descriptors have been processed using a variety of machine learning algorithms: RF LASSO, Ridge, EN and SVM to generate a predictive model. The properties were obtained with Multiwfn, AIMALL and VMD programs. Docking simulations were performed by using AutoDock 4.2 and LigPlot + programs. All the calculations in this work are carried out in Gaussian 16 program package.

The chemical reactivity and QSPR of organic compounds applied to dye-sensitized solar cells using DFT.

The structural and electronic properties were calculated for seventy organic compounds used as dye sensitizers in solar cells, applying the B3LYP exchange-correlation energy functional with the 6-311G∗∗ basis set. Moreover, the present study proposes two new quantitative structure-property relationship (QSPR) models that enable the prediction of the power conversion efficiency (PCE) and maximum absorption wavelength (λmax) of these systems, the two QSPR models were validated using the coefficient of determination (R2) of 0.62 for both models with the leave-one-out cross-validation correlation coefficient (Q2LOO) of 0.55 and 0.57, respectively. Furthermore, applicability domain analysis was conducted in order to identify the related compounds via the extrapolation of the model.

Hibiscus Acid and Chromatographic Fractions from Hibiscus Sabdariffa Calyces: Antimicrobial Activity against Multidrug-Resistant Pathogenic Bacteria.

The anti-microbial properties of acetone extracts from Hibiscus sabdariffa calyces, fractions isolated by silica gel chromatography and hibiscus acid purified from some of these fractions and additionally identified by nuclear magnetic resonance spectroscopy, mid-infrared spectroscopy and X-ray diffraction, were studied against both multidrug-resistant Salmonella strains and pathogenic Escherichia coli bacteria. Gel diffusion was used to determine the anti-microbial effects. The mode of action of hibiscus acid was determined by crystal violet assay. Hibiscus acid and 17 of the 25 chromatographic fractions obtained, displayed an anti-microbial effect against all bacterial strains tested. Hibiscus acid showed a greater anti-microbial effect than the acetone extract against most of the bacteria strains, while chromatographic fractions IX-XIV exerted the greatest anti-microbial effect against all bacteria. The minimum inhibitory concentration of the acetone extract was 7 mg/mL, and the minimum bactericidal concentration was 10 mg/mL, while the corresponding values for hibiscus acid were 4-7 and 7 mg/mL, respectively. The results of the crystal violet assay indicate that hibiscus acid alters membrane permeability. Hibiscus acid is a potential alternative to control multidrug-resistant bacteria. Due to its ready availability and easy extraction from H. sabdariffa, hibiscus acid is potentially useful in the food industries.