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Modeling of Nanomaterials for Safety Assessment: From Regulatory Requirements to Supporting Scientific Theories
Published in Agnieszka Gajewicz, Tomasz Puzyn, Computational Nanotoxicology, 2019
Lara Lamon, David Asturiol, Karin Aschberger, Jos Bessems, Kirsten Gerloff, Andrea-Nicole Richarz, Andrew Worth
A descriptor is a mathematical representation of the chemical structure [220] and has been defined as follows: The molecular descriptor is the final result of a logic and mathematical procedure which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment.
A descriptor-based analysis to highlight the mechanistic rationale of mutagenicity
Published in Journal of Environmental Science and Health, Part C, 2021
Domenico Gadaleta, Emilio Benfenati
Quantitative Structure-Activity Relationships (QSARs) are example of in silico approaches that aim at establishing a quantitative relation between an endpoint (e.g. mutagenicity) and the structure of chemicals. The need of representing quantitatively the structural information is resolved with the use of molecular descriptors. A molecular descriptor is a numerical value obtained via particular mathematical treatments encoding several kinds of structural, bio-physical or physico-chemical features of a molecule.12 A set of molecular descriptors calculated for a set of compounds are the independent variables in the mathematical equation that is the QSAR model. Thousands of molecular descriptors have been proposed that are derived from different theories and approaches.12 It is commonly recognized the importance to keep low the number of descriptors used for QSAR model derivation, because a large number of descriptors often leads to chance-correlation13 and returns models that exceed in complexity and are difficult to be interpreted.14
Future Challenges in the Modelling and Simulations of High-pressure Flows
Published in Combustion Science and Technology, 2020
Other methods of determining these interaction parameters exist. For example, Abudour et al. (2014) use a quantitative structure–property relationship modeling approach wherein first a compilation of a representative binary VLE database is obtained and then the of the PR EOS are regressed for each binary system. Further, two-dimensional molecular structures of components in each binary system are generated and optimized to find the three-dimensional conformation with the lowest energy, as these are considered stable states of the molecules. These optimized molecular structures are used to calculate molecular descriptors for each component; the molecular descriptor transforms chemical information encoded within a symbolic representation of a molecule into useful data for a particular purpose. These descriptors are then used to develop an artificial neural network model of Li et al. (2016) developed a generalized temperature-dependent interaction-coefficient correlation for CO/heavy-n-alkanes binary systems by using the PR EOS where is proportional to a function of Tred and an extensive VLE dataset was used for determining the interaction coefficients. The results show that the interaction coefficient values are both sensitive to and the carbon number of n-alkane. Most important, the authors develop an interaction-coefficient correlation which can be incorporated in the attractive term of the PR EOS. Extensive results were obtained with accuracies that deviate from the data by less than 7%.