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Phytochemicals are now increasingly exploited as remedial agents for the management of diabetes due to side effects attributable to commercial antidiabetic agents. This study investigated the structural and molecular mechanisms by which betulinic acid exhibits its antidiabetic effect via in vitro and computational techniques. In vitro antidiabetic potential was analysed via on α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin inhibitory assays. Its structural and molecular inhibitory mechanisms were investigated using Density Functional Theory (DFT) analysis, molecular docking and molecular dynamics (MD) simulation. Betulinic acid significantly (p < 0.05) inhibited α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin enzymes with IC50 of 70.02 μg/mL, 0.27 μg/mL, 1.70 μg/mL and 8.44 μg/mL, respectively. According to DFT studies, betulinic acid possesses similar reaction in gaseous phase and water due to close values observed for highest occupied molecular orbital (HOMO) and lowest occupied molecular orbital (LUMO) and the chemical descriptors. The dipole moment indicates that betulinic acid has high polarity. Molecular electrostatic potential surface revealed the electrophilic and nucleophilic attack-prone atoms of the molecule. Molecular dynamic studies revealed a stable complex between betulinic acid and α-amylase, α-glucosidase, pancreatic lipase and α-chymotrypsin. The study elucidated the potent antidiabetic properties of betulinic acid by revealing its conformational inhibitory mode of action on enzymes involved in the onset of diabetes. [ABSTRACT FROM AUTHOR]

Property-Encoded Surface Translator (PEST) descriptors were found to be correlated with the degradation rates of polycyclic aromatic hydrocarbons (PAHs) by the peroxy-acid process. Reaction rate constants (k) in hr-1for nine PAHs (acenaphthene, anthracene, benzo[a]pyrene, benzo[k]fluoranthene, fluoranthene, fluorene, naphthalene, phenanthrene, and pyrene) were determined by a peroxy-acid treatment method that utilized acetic acid, hydrogen peroxide, and a sulphuric acid catalyst to degrade the polyaromatic structures. Molecular properties of the selected nine PAHs were derived from structures optimized at B3LYP/6-31G(d) and HF/6-31G(d) levels of theory. Properties of adiabatic and vertical ionization potential (IP), highest occupied molecular orbitals (HOMO), HOMO/lowest unoccupied molecular orbital (LUMO) gap energies and HOMO/singly occupied molecular orbital (SOMO) gap energies were not correlated with rates of peroxy-acid reaction. PEST descriptors were calculated from B3LYP/6-31G(d) optimized structures and found to have significant levels of correlation withk. PIP Min described the minimum local IP on the surface of the molecule and was found to be related tok. PEST technology appears to be an accurate method in predicting reactivity and could prove to be a valuable asset in building treatment models and in remediation design for PAHs and other organic contaminants in the environment. [ABSTRACT FROM AUTHOR]

Calculated energies of lowest unoccupied molecular orbitals (LUMOs) are frequently employed as descriptors in studies of quantitative structure–activity relationships and linear free energy relationships involving electron transfer. However, the quantum chemical programs with which these are carried out, whether Hartree–Fock or density functional theory, do not treat orbitals of different character, for example, C=C π∗and C-Cl σ∗, consistently, nor is there consistency among different families of compounds. These problems can be ameliorated with the use of the experimental equivalent of the LUMO energy, the vertical attachment energy (VAE), or by shifting and scaling LUMO energies to a training set of available VAEs in similar compounds. Examples from the literature are used to illustrate these points. [ABSTRACT FROM AUTHOR]

The electronic properties of 29 polychlorinated dibenzo-p-dioxins and dibenzofurans and dioxin-like polychlorinated biphenyls that have been included in the toxic equivalency factor system have been investigated and used to derive quantum mechanical (QM) chemical descriptors for QSAR modelling. Their utility in this context was investigated alongside descriptors based on ultraviolet absorption data and traditional 2D descriptors including log Kow, polarizability, molecular surface properties, van der Waals volume and selected connectivity indices. The QM descriptors were calculated using the semi-empirical AM1 method and the density functional theory method B3–LYP/6–31G∗∗. Atom-specific and molecular quantum chemical descriptors were calculated to compare the electronic properties of dioxin-like compounds regardless of their chemical class, with particular emphasis on the lateral positions. Multivariate analysis revealed differences between the chemical classes in terms of their electronic properties and also highlighted differences between congeners. The results obtained demonstrated the importance of considering molecular orbital energies, but also indicated that the ratios of the coefficients of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) at the lateral carbons were important. In addition, the digitalized UV spectra contained chemical information that provided crucial insights into dioxin-like activity. [ABSTRACT FROM AUTHOR]

Quantitative structure–activity relationship analysis and estimation of toxicological effects at lower-mid trophic levels provide first aid means to understand the toxicity of chemicals. Daphnia magna serves as a good starting point for such toxicity studies and is also recognized for regulatory use in estimating the risk of chemicals. The ECOTOX database was queried and analysed for available data and a homogenous subset of 253 compounds for the endpoint LC50 48 h was established. A four-parameter quantitative structure–activity relationship was derived (coefficient of determination, r 2 = 0.740) for half of the compounds and internally validated (leave-one-out cross-validated coefficient of determination,  = 0.714; leave-many-out coefficient of determination,  = 0.738). External validation was carried out with the remaining half of the compounds (coefficient of determination for external validation,  = 0.634). Two of the descriptors in the model (log P, average bonding information content) capture the structural characteristics describing penetration through bio-membranes. Another two descriptors (energy of highest occupied molecular orbital, weighted partial negative surface area) capture the electronic structural characteristics describing the interaction between the chemical and its hypothetic target in the cell. The applicability domain was subsequently analysed and discussed. [ABSTRACT FROM AUTHOR]

Quantitative structure–activity relationship (QSAR) studies were conducted on an in-house database of cytochrome P450 enzyme 1A2 inhibitors using the comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA) and hologram QSAR (HQSAR) approaches. The database consisted of 36 active molecules featuring varied core structures. The model based on the naphthalene substructure alignment incorporating 19 molecules yielded the best model with a CoMFA cross validation value q2 of 0.667 and a Pearson correlation coefficient r2 of 0.976; a CoMSIA q2 value of 0.616 and r2 value of 0.985; and a HQSAR q2 value of 0.652 and r2 value of 0.917. A second model incorporating 34 molecules aligned using the benzene substructure yielded an acceptable CoMFA model with q2 value of 0.5 and r2 value of 0.991. Depending on the core structure of the molecule under consideration, new CYP1A2 inhibitors will be designed based on the results from these models. [ABSTRACT FROM AUTHOR]

The effects of eight transition metals were studied in a nitrifying system to investigate the relationship between the ionic characteristics of metals and their toxicity to nitrifiers. The cumulative oxygen consumption and the cumulative carbon dioxide production were monitored throughout each respirometric batch run to determine the toxicity of metals to nitrifiers. Several quantitative cationic-activity relationship (QCAR) models were developed on the basis of these different toxicity endpoints using quantum chemical descriptors. Descriptors were calculated with density functional theory (DFT) at the B3LYP/LANL2DZ level using the Gaussian 03W software. Additionally, the same descriptors were recalculated using another basis set, B3LYP/SDD, to test the impact of the basis set on prediction of toxicity. Of the calculated descriptors, mainly the gaseous phase descriptors explained significant variances in both toxicity endpoints. However, the energy of the polarized solute-solvent (EPSS) was the most important common descriptors in modelling labile toxicity. A combination of the aqueous phase energy of the highest occupied molecular orbital (EHOMO(aq)) and the maximum value for the energy of the lowest unoccupied molecular orbital of the most important metal species (ELUMOCmax) produced the best two-descriptor model for both pTO2 and pTCO2. The electron donor/acceptor ability of metals and the electron acceptor ability of metal species (ELUMOCmax) seemed to be important in explaining toxicity in aqueous media regardless of the measured endpoints for nitrifiers. [ABSTRACT FROM AUTHOR]

Some indolo[2,1-b]quinalozine (tryptanthrin) analogues present cytotoxic activity against human breast cancer cells. In this work, chemometric methods were applied in the search for building discriminant models between active and inactive analogues, based on the correlations among their in vitro cytotoxic activities and their electronic and geometric molecular descriptors. From 88 descriptors calculated with density functional theory with the exchange correlation functional B3LYP and the basis set 6-31G* (Gaussian 03), 29 were pre-selected based on their Fisher weights, and finally five descriptors (partial charge on atom 15, bond orders between atoms 12-13, 17-25 and 18-26, and energy difference between frontier molecular orbitals) were selected for principal component analysis. This analysis was able to discriminate 12 inactive from 22 active analogues by using only one principal component, accounting for 49% of the total variance and allowing us to better understand the influence of these electronic descriptors in the cytotoxic activity. In addition, a supervised partial least-squares discriminant model was build and successfully used to discriminate tryptanthrin analogues. The model was validated through an independent test set and considered robust to overfitting. [ABSTRACT FROM AUTHOR]

A wide range of physicochemical properties based on molecular topology, size and shape, and semi-empirical molecular orbital theory were calculated for a variety of dermal and respiratory sensitizers, as well as some non-active substances. Compounds were randomly selected to belong to a training set of substances (approximately 90%) for development of quantitative structure-activity relationship (QSAR) models or to a test set (approximately 10%) for testing the models. A choice was made of those descriptors which were related to sensitization using standard statistics. Pattern recognition methods were then utilized to identify the combination of properties that provided the greatest contribution to the observed biological effect. Principal components (PC) analysis was then performed on the most important properties. The models derived were then applied to a test set of known sensitizers to predict their class. For dermal and respiratory sensitizers respectively, the PC model classified five (100%) of the R-43 active and two (100%) of the R42-active test set compounds correctly. Analysis of the PC loadings showed that the most useful properties distinguishing respiratory and/or dermal sensitizers from inactive substances were the molecular orbital-based terms. [ABSTRACT FROM AUTHOR]

Skin sensitisation is a key endpoint under REACH as it is costly and its assessment currently has a high dependency on animal testing. In order to reduce both the cost and the numbers of animals tested, it is likely that (quantitative) structure-activity relationships ((Q)SAR) and read-across methods will be utilised as part of intelligent testing strategies. The majority of skin sensitisers elicit their effect via covalent bond formation with skin proteins. These reactions have been understood in terms of well defined nucleophilic-electrophilic reaction chemistry. Thus, a first step in (Q)SAR analysis is the assignment of a chemical's potential mechanism of action enabling it to be placed in an appropriate reactivity domain. The aim of this study was to design a series of SMARTS patterns capable of defining these reactivity domains. This was carried out using a large database of local lymph node assay (LLNA) results that had had potential mechanisms of action assigned to them using expert knowledge. A simple algorithm was written enabling the SMARTS patterns to be used to screen a database of SMILES strings. The SMARTS patterns were then evaluated using a second, smaller, test set of LLNA results which had also had potential mechanisms of action assigned by experts. The results showed that the SMARTS patterns provided an excellent method of identifying potential electrophilic mechanisms. The findings are supported, in part, by molecular orbital calculations which confirm assignment of reactive mechanism of action. The ability to define a chemical's potential reaction mechanism is likely to be of significant benefit to regulators and risk assessors as it enables category formation and subsequent read-across to be performed. [ABSTRACT FROM AUTHOR]

The hydrohaloalkanes have attracted much attention as potential substitutes of chlorofluomcarbons (CFCs) that deplete the ozone layer and lead to great high global warming. Having a short atmospheric lifetime is very. important for the potential substitutes that may also induce ozone depletion and yield high global warming gases to be put in use. Quantitative structure-activity relationship (QSAR) studies were presented for their lifetimes aided by the quantum chemistry parameters including net charges, Mulliken overlaps, EHOMO and ELUMO based on the density functional theory (DFT) at B3PW91 level, and the C-H bond dissociation energy based on AMI calculations, Outstanding features of the logistic mapping, a simple chaotic system, especially the inherent ability to search the space of interest exhaustively have been utilized. The chaotic mapping aided genetic algorithm artificial neural network training scheme (CGANN) showed better performance titan the conventional genetic algorithm ANN training when the structure of the data set was not favorable. The lifetimes of HFCs and HCs appeared to be greatly dependent on their energies of the highest occupied molecular orbitals. The perference of the RMSRE comparing to RMSE as objective function of ANN training was better for the samples of interest with relatively short lifetimes. C2H6 and C3H8 as potential green substitutes of CFCs present relatively short lifetimes. [ABSTRACT FROM AUTHOR]