The (Q)SAR models for evaluating the structure–property relationships, fi t for prediction of drug interactions with P-glycoprotein as inhibitors or substrates, were constructed using PASS and GUSAR software. The models were constructed and validated on the basis of information on the structure and characteristics of 256 and 94 compounds used as P-glycoprotein substrates and inhibitors, respectively. The initial samples were divided 80:20 into training and test samples. The best prediction accuracy for the test samples was 78% for P-glycoprotein substrate prediction (PASS) and 89% for inhibitor prediction (GUSAR).
Bulletin of Experimental Biology and Medicine, 2013, 154 (4), 521-524.

The evaluation of possible interactions between chemical compounds and antitarget proteins is an important task of the research and development process. Here, we describe the development and validation of QSAR models for the prediction of antitarget end-points, created on the basis of multilevel and quantitative neighborhoods of atom descriptors and selfconsistent regression. Data on 4000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes, and 3 transporters) were used to model 32 sets of end-points (IC50, Ki, and Kact). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95%, and for half of the test sets, it was 100%. The accuracy of prediction for 29 of the endpoints, based on the external test sets, was typically in the range of R2 test = 0.6− 0.9; three tests sets had lower R2 test values, specifically 0.55−0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the antitarget end-points and high coverage for all external test sets. On the basis of the created models, we have developed a freely available online service for in silico prediction of 32 antitarget end-points: http://www.pharmaexpert.ru/ GUSAR/antitargets.html.
Chemical Research in Toxicology, 2012, 25 (11) 2378-2385.

Р-гликопротеин (Pgp) является одним из основных АВС-транспортеров гематоэнцефалического барьера, осуществляющих выведение из мозга в кровоток различных органических соединений, включая лекарства. Поэтому при создании новых лекарственных препаратов, необходимо учитывать их взаимодействие с Pgp в качестве субстратов или ингибиторов. C использованием компьютерных программ PASS и GUSAR мы построили (Q)SAR модели для оценки взаимосвязей «структура–свойство». При построении и валидации моделей была использована информация о структуре и свойствах 256 и 94 соединений, исследованных в качестве субстратов и ингибиторов Р-гликопротеина. Исходные выборки были разделены на обучающую и тестовую в пропорции 80:20. Лучшая точность предсказания, полученная для тестовых выборок, составила 78% при прогнозе субстратов (PASS) и 89% при прогнозе ингибиторов (GUSAR) Pgp.
Бюллетень экспериментальной биологии и медицины, 2012, 154 (10), 520-524.

A series of 2-arylhydroxynitroindoles were prepared and tested for antifungal activity in vitro. The preliminary bioassays indicated that some compounds are comparable to the commercial fungicide (triadimefon). To further explore the structureeactivity relationships, the data set of the seventeen structures and their quantitative values of antifungal activities were used for QSAR modeling. Based on the obtained QSAR models four new chemical compounds were designed, synthesized and tested in fungicidal assays. Reasonable correspondence between the experimental and predicted values of antifungal activity was observed.
Eur. J. Med. Chem., 2011, 46 (9), 4374-4382.

In the existing QSAR methods any molecule is represented as a single point in many-dimensional space of molecular descriptors. We proposed a new QSAR approach based on Quantitative Neighbourhoods of Atoms (QNA) descriptors, which characterize each atom of a molecule and depend on the whole molecule structure. In “Star Track” methodology any molecule is represented as a set of points in two-dimensional space of QNA descriptors. By our new method the estimate of target property of chemical compound is calculated as the average value of the function of QNA descriptors in the points of the atoms of a molecule in QNA descriptors’ space. Substantially, we have proposed to use only two instead of more than three thousand molecular descriptors applying in QSAR. On the basis of this approach we have developed computer program GUSAR and compared it with the several widely used QSAR methods including CoMFA, CoMSIA, Golpe/GRID, HQSAR and others, using ten data sets representing various chemical series and diverse types of biological activity. It was shown that in the majority of cases the accuracy and predictivity of GUSAR models appeared to be better than for the reference QSAR methods. High predictive ability and robustness of GUSAR were also shown in leave-20%-out cross-validation procedure.
SAR and QSAR in Environmental Research, 2009, 20 (7-8), 679-709.