Andrey A. Toropov

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Quantitative structure-activity relationships (QSAR) for prediction of binding affinities (pEC50, i.e., minus decimal logarithm of the 50% effective concentration) of 20 fullerene derivatives inhibitors of the HIV-1 PR (human immunodeficiency virus type 1 protease) have been developed by application of the optimal descriptors approach calculated with SMILES(More)
For six random splits, one-variable models of rat toxicity (minus decimal logarithm of the 50% lethal dose [pLD50], oral exposure) have been calculated with CORAL software ( The total number of considered compounds is 689. New additional global attributes of the simplified molecular input line entry system (SMILES) have been(More)
Optimal descriptors calculated with the simplified molecular input line entry system (SMILES) have been utilized in modeling of carcinogenicity as continuous values (logTD(50)). These descriptors can be calculated using correlation weights of SMILES attributes calculated by the Monte Carlo method. A considerable subset of these attributes includes rare(More)
Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool (in silico) to rapidly predict various endpoints in general, and drug toxicity in particular. However, this dynamic evolution of experimental data (expansion of existing experimental data on drugs toxicity) leads to the problem of critical estimation of the data. The(More)
The SMILES (simplified molecular input line entry system) nomenclature was used to elucidate the molecular structure in constructing the quantitative structure-property/activity relationships (QSPR/QSAR) for predicting quail toxicity after oral exposure. The presence of chemical elements in different electronic states (e.g., C, c, O, o, Cl, Br, etc.) and 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 (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)
A large set of organic compounds (n=906) has been used as a basis to build up a model for the odor threshold (mg/m(3)). The statistical characteristics of the best model are the following: n=523, r(2)=0.647, RMSE=1.18 (training set); n=191, r(2)=0.610, RMSE=1.03, (calibration set); and n=192, r(2)=0.686, RMSE=1.06 (validation set). A mechanistic(More)
Quantitative structure - activity relationships (QSARs) for the pIC50 (binding affinity) of gamma-secretase inhibitors can be constructed with the Monte Carlo method using CORAL software ( The considerable influence of the presence of rings of various types with respect to the above endpoint has been detected. The mechanistic(More)