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We propose spectral density distribution moments as molecular descriptors. We apply the new descriptors for developing a QSPR model that predicts the logarithmic values of subcooled liquid vapor pressure. We consider the infrared spectra of chloronaphthalenes. [ABSTRACT FROM AUTHOR]

Nowadays nanotechnology is one of the most promising areas of science. The number and quantity of synthesized nanomaterials increase exponentially, therefore it is reasonable to expect that comprehensive risk assessment based only on empirical testing of all novel engineered nanoparticles (NPs) will very soon become impossible. Hence, the development of computational methods complementary to experimentation is very important. Quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) models widely used in pharmaceutical chemistry and environmental science can also be modified and adopted for nanotechnology to predict physico-chemical properties and toxicity of empirically untested nanomaterials. All QSPR/QSAR modelling activities are based on experimentally derived data. It is important that, within a given data set, all values should be consistent, of high quality and measured according to a standardized protocol. Unfortunately, the amount of such data available for engineered nanoparticles in various data sources (i.e. databases and the literature) is very limited and seldom measured with a standardized protocol. Therefore, we have proposed a framework for collecting and evaluating the existing data, with the focus on possible applications for computational evaluation of properties and biological activities of nanomaterials. [ABSTRACT FROM AUTHOR]

Molecular descriptors from calculations at the level of Density Functional Theory (B3LYP/6-311++G**) were effectively applied in QSPR estimation of supercooled liquid vapour pressures (PL) for individual chloronaphthalene congeners. The estimated values of log PL varied from 1.05 Pa to 5.6 × 10-5 Pa, depending on the number of chlorine substituents present in the molecule and the substitution pattern. Comparison of the five chemometrical methods of modelling (approaches) led to the final conclusion, that the use of relatively simple PLS combined with one of the variable pre-selection algorithms (UVE or GA) seems to be the optimal choice in such computational studies for persistent organic pollutants. The best GA-PLS model was characterized by the value of root mean square error of prediction RMSEP = 0.108 logarithmic Pascal units. [ABSTRACT FROM AUTHOR]

Based on available toxicological data and matrix of structural descriptors 7-ethoxyresorufin-O-diethylase (EROD) and luciferase (luc) inducing potency for 75 congeners of chloronaphthalene was predicted using quantitative structure– activity relationships (QSAR) strategy. The most active congeners in EROD and luciferase bioassays were CN congeners nos. 75 and 67. Some empirical rules describing toxic PCNs were formulated. [ABSTRACT FROM AUTHOR]

The unlimited possibility of modifying the structure of the cation and anion of a given ionic liquid (IL) delivers a countless number of potential derivatives having different toxicological activities. The experimental measurements for such huge set of compounds are impossible due to time and costs limitations. In the absence of experimental data, computational methods such as quantitative structure–activity relationship (QSAR) method can be applied to characterize those species for which experimental data is not available. However, two different ways of QSAR modeling could be applied: local modeling - dedicating to one specific group and more complex global models. The aim of this study was to investigate how local/global QSAR modeling works for the ionic liquids. Which of these two approaches is more suitable in ILs' modeling? We have developed six local models and a global one for predicting the toxicity against the leukemia rat cell line IPC-81. Based on the obtained results, we conclude that local models are useful to solve specific mechanistic problems among one group of ILs, but in case of predicting the toxicity for a new designed compound the global model, whenever fulfills all quality recommendations, is recommended. [ABSTRACT FROM AUTHOR]