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Toropova A.P.,Laboratory of Environmental Chemistry and Toxicology | Toropov A.A.,Laboratory of Environmental Chemistry and Toxicology
Letters in Drug Design and Discovery | Year: 2016

Background: The prediction of physicochemical properties is important task of the natural sciences. Quantitative structure - property relationships (QSPR) are a tool to solve the task. Objective: QSPR for dispersibility of graphene in various organic solvents has been built up by means of the CORAL software (http://www.insilico.eu/coral). Method: The Monte Carlo technique is the basis of the models for dispersibility of graphene in various organic solvents. Simplified molecular input-line entry systems (SMILES) are used to represent the molecular structure for the QSPR analysis. In other words, the graphene dispersibility is modeled as a mathematical function of the molecular structure. Results: The statistical characteristics of the models are quite good. They have the mechanistic interpretation: the structural features of molecules of solvents which are promoters of increase or decrease of graphene dispersibility have been discovered. Conclusion: The suggested approach can be used to predict dispersibility of graphene in various organic solvents. © 2016 Bentham Science Publishers. Source


Veselinovic J.B.,University of Nis | Veselinovic A.M.,University of Nis | Toropova A.P.,Laboratory of Environmental Chemistry and Toxicology | Toropov A.A.,Laboratory of Environmental Chemistry and Toxicology
European Journal of Medicinal Chemistry | Year: 2016

Quantitative structure - activity relationships (QSARs) for the Lowest Observed Adverse Effect Level (LOAEL) for a large set of organic compounds (n = 341) are suggested. The molecular structures of these compounds are represented by Simplified Molecular Input-Line Entry Systems (SMILES). A criteria for the estimation quality of split into the "visible" training set (used for developing a model) and "invisible" external validation set is suggested. The correlation between the above criterion and the predictive potential of developed QSAR model (root-mean-square error for "invisible" validation set) has been detected. One-variable models are built up for several different splits into the "visible" training set and "invisible" validation set. The statistical quality of these models is quite good. Mechanistic interpretation and the domain of applicability for these models are defined according to probabilistic point of view. The methodology for defining applicability domain in QSAR modeling with SMILES notation based optimal descriptors is presented. © 2016 Elsevier Masson SAS. All rights reserved. Source


Cappelli C.I.,Laboratory of Environmental Chemistry and Toxicology | Benfenati E.,Laboratory of Environmental Chemistry and Toxicology | Cester J.,Rovira i Virgili University
Environmental Research | Year: 2015

The partition coefficient (logP) is a physicochemical parameter widely used in environmental and health sciences and is important in REACH and CLP regulations. In this regulatory context, the number of existing experimental data on logP is negligible compared to the number of chemicals for which it is necessary. There are many models to predict logP and we have selected a number of free programs to examine how they predict the logP of chemicals registered for REACH and to evaluate wheter they can be used in place of experimental data. Some results are good, especially if the information on the applicability domain of the models is considered, with R2 values from 0.7 to 0.8 and root mean square error (RMSE) from 0.8 to 1.5. © 2015. Source


Toropov A.A.,Laboratory of Environmental Chemistry and Toxicology | Toropova A.P.,Laboratory of Environmental Chemistry and Toxicology | Benfenati E.,Laboratory of Environmental Chemistry and Toxicology | Gini G.,Polytechnic of Milan | And 3 more authors.
Biochemical and Biophysical Research Communications | Year: 2013

Quantitative structure - activity relationships (QSARs) developed to evaluate percentage of inhibition of STa-stimulated (Escherichia coli) cGMP accumulation in T84 cells are calculated by the Monte Carlo method. This endpoint represents a measure of biological activity of a substance against diarrhea. Statistical quality of the developed models is quite good. The approach is tested using three random splits of data into the training and test sets. The statistical characteristics for three splits are the following: (1) n=20, r2=0.7208, q2=0.6583, s=16.9, F=46 (training set); n=11, r2=0.8986, s=14.6 (test set); (2) n=19, r2=0.6689, q2=0.5683, s=17.6, F=34 (training set); n=12, r2=0.8998, s=12.1 (test set); and (3) n=20, r2=0.7141, q2=0.6525, s=14.7, F=45 (training set); n=11, r2=0.8858, s=19.5 (test set). Based on the proposed here models hypothetical compounds which can be useful agents against diarrhea are suggested. © 2013 Elsevier Inc. Source


Manganaro A.,Laboratory of Environmental Chemistry and Toxicology | Manganaro A.,Kode s.r.l | Pizzo F.,Laboratory of Environmental Chemistry and Toxicology | Lombardo A.,Laboratory of Environmental Chemistry and Toxicology | And 2 more authors.
Chemosphere | Year: 2016

The ability of a substance to resist degradation and persist in the environment needs to be readily identified in order to protect the environment and human health. Many regulations require the assessment of persistence for substances commonly manufactured and marketed. Besides laboratory-based testing methods, in silico tools may be used to obtain a computational prediction of persistence. We present a new program to develop k-Nearest Neighbor (. k-NN) models. The k-NN algorithm is a similarity-based approach that predicts the property of a substance in relation to the experimental data for its most similar compounds. We employed this software to identify persistence in the sediment compartment. Data on half-life (HL) in sediment were obtained from different sources and, after careful data pruning the final dataset, containing 297 organic compounds, was divided into four experimental classes. We developed several models giving satisfactory performances, considering that both the training and test set accuracy ranged between 0.90 and 0.96. We finally selected one model which will be made available in the near future in the freely available software platform VEGA. This model offers a valuable in silico tool that may be really useful for fast and inexpensive screening. © 2015 Elsevier Ltd. Source

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