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      QSPR Modeling of Potentiometric Mg 2+ /Ca 2+ Selectivity for PVC‐plasticized Sensor Membranes

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          Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles.

          It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years, and there is a need for new methods to quickly test the potential toxicity of these materials. Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.
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            Computational methods in developing quantitative structure-activity relationships (QSAR): a review.

            Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain.
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              Calculation of solvation free energies of charged solutes using mixed cluster/continuum models.

              We derive a consistent approach for predicting the solvation free energies of charged solutes in the presence of implicit and explicit solvents. We find that some published methodologies make systematic errors in the computed free energies because of the incorrect accounting of the standard state corrections for water molecules or water clusters present in the thermodynamic cycle. This problem can be avoided by using the same standard state for each species involved in the reaction under consideration. We analyze two different thermodynamic cycles for calculating the solvation free energies of ionic solutes: (1) the cluster cycle with an n water cluster as a reagent and (2) the monomer cycle with n distinct water molecules as reagents. The use of the cluster cycle gives solvation free energies that are in excellent agreement with the experimental values obtained from studies of ion-water clusters. The mean absolute errors are 0.8 kcal/mol for H(+) and 2.0 kcal/mol for Cu(2+). Conversely, calculations using the monomer cycle lead to mean absolute errors that are >10 kcal/mol for H(+) and >30 kcal/mol for Cu(2+). The presence of hydrogen-bonded clusters of similar size on the left- and right-hand sides of the reaction cycle results in the cancellation of the systematic errors in the calculated free energies. Using the cluster cycle with 1 solvation shell leads to errors of 5 kcal/mol for H(+) (6 waters) and 27 kcal/mol for Cu(2+) (6 waters), whereas using 2 solvation shells leads to accuracies of 2 kcal/mol for Cu(2+) (18 waters) and 1 kcal/mol for H(+) (10 waters).
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Electroanalysis
                Electroanalysis
                Wiley
                1040-0397
                1521-4109
                April 2020
                January 13 2020
                April 2020
                : 32
                : 4
                : 792-798
                Affiliations
                [1 ]Institute of ChemistrySaint-Petersburg State University, Peterhof Universitetsky Prospect, 26 Saint-Petersburg 198504 Russia
                [2 ]A.N. Frumkin Institute of Physical Chemistry and ElectrochemistryRussian Academy of Sciences Leninskiy Prosp., 31 119071 Moscow Russia
                [3 ]Laboratoire de Chémoinformatique, UMR 7140 CNRSUniversité de Strasbourg 4, rue Blaise Pascal 67000 Strasbourg France
                Article
                10.1002/elan.201900648
                © 2020

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