Andrey A. Toropov

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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(More)
Optimal descriptors based on the simplified molecular input line entry system (SMILES) have been utilized in modeling of acute toxicity towards rats. Toxicity of 61 benzene derivatives has been modeled by means of balance of correlations for sets of the training (n=27) and calibration (n=24). The obtained models were evaluated with the external test set(More)
A predictive quantitative structure - activity relationships model of arylpiperazines as high-affinity 5-HT(1A) receptor ligands was developed using CORAL software (http://www.insilico.eu/CORAL). Simplified molecular input-line entry system (SMILES) was used as representation of the molecular structure of the arylpiperazines. The balance of correlations was(More)
Quantitative structure-activity relationships (QSARs) were developed for three sets of toxicity data. Chemicals in each set represented a number of narcoses and electrophilic mechanisms of toxic action. A series of quantitative structure-toxicity models correlating toxic potency with a number of optimization of correlation weights of local graph invariants(More)
Optimal descriptors based on the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity. Carcinogenicity of 401 compounds has been modeled by means of balance of correlations for the training (n = 170) and calibration (n = 170) sets. The obtained models were evaluated with an external test set (n = 61).(More)
Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of(More)
The optimization of correlation weights scheme was applied to model lipid-water partition coefficient (log P) of two sets of diverse functional aliphatic and aromatic compounds. In both cases, the optimized descriptors formulated based on the data of training sets generated statistically acceptable relations for the corresponding training sets, test sets,(More)
Quantitative structure-activity relationships (QSAR) for no observed adverse effect levels (NOAEL, mmol/kg/day, in logarithmic units) are suggested. Simplified molecular input line entry systems (SMILES) were used for molecular structure representation. Monte Carlo method was used for one-variable models building up for three different splits into the(More)
Convenient to apply and available on the Internet software CORAL (http://www.insilico.eu/CORAL) has been used to build up quantitative structure-activity relationships (QSAR) for prediction of cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of concentration for 50% effect pEC50). In this study six random splits of the(More)
We used simplified molecular input line entry system to construct optimal descriptors for modelling of the mutagenic potency of heteroaromatic amines by quantitative structure-activity relationships. Statistical characteristics of the model are n = 67, r(2) = 0.8639, r(2) (CV) = 0.8560, s = 0.737, F = 413 (training set); and n = 28, r(2) = 0.8760, r(2) (CV)(More)