The way of storage capacity prediction had been proposed for multiservice distribution system on the basis of simulation statistic modeling with auto hold up method using. Statistic algorithm had been designed for prediction purpose. Simulation model had been deployed on the basis of statistic algorithm.
Проблеми телекомунікацій. – 2011. – № 2 (4). – С. 108 – 117.

The method for QSAR modelling of rat acute toxicity based on the combination of QNA (Quantitative Neighbourhoods of Atoms) descriptors, PASS (Prediction of Activity Spectra for Substances) predictions and self-consistent regression (SCR) is presented. PASS predicted biological activity profiles are used as independent input variables for QSAR modelling with SCR. QSAR models were developed using LD50 values for compounds tested on rats with four types of administration (oral, intravenous, intraperitoneal, subcutaneous). The proposed method was evaluated on the set of compounds tested for acute rat toxicity with oral administration (7286 compounds) used for testing the known QSAR methods in T.E.S.T. 3.0 program (U.S. EPA). The several other sets of compounds tested for acute rat toxicity by different routes of administration selected from SYMYX MDL Toxicity Database were used too. The method was compared with the results of prediction of acute rodent toxicity for noncongeneric sets obtained by ACD/Labs Inc. The test sets were predicted with regards to the applicability domain. Comparison of accuracy for QSAR models obtained separately using QNA descriptors, PASS predictions, nearest neighbours’ assessment with consensus models clearly demonstrated the benefits of consensus prediction. Free available web-service for prediction of LD50 values of rat acute toxicity was developed: http://www.pharmaexpert.ru/GUSAR/AcuToxPredict/
Molecular Informatics, 2011, 30 (2-3), 241–250.

Background: The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published in silico method PAAS was applied for prediction of interactions between protein kinases and their substrates. Results: We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH® database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell. Conclusions: It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at http://www.ibmc.msk.ru/PAAS/.
BMC Bioinformatics, 2010, 11: 313.

PASS INet system for prediction of biological activity spectra via Internet is described.
Bioinformatics, 2000, 16 (8), 747-748.

The concept of Biological Activity Spectrum served as a basis for developing PASS (Prediction of Activity Spectra for Substances) software product. PASS predicts simultaneously more than 780 pharmacological effects and biochemical mechanisms based on the structural formula of a substance. It may be used for finding new targets (mechanisms) for known pharmaceuticals and for searching new biologically active substances. PASS prediction ability was evaluated by activity spectra prediction for 63 substances that are presented in the Molecule of the Month section of Prous Science (http://www.prous.com), belong to different chemical classes and reveal various types of biological activity. Mean accuracy of prediction turned out to be about 90%; therefore, it is reasonable to use PASS for finding and optimizing new lead compounds. A web-site with a new internet version of PASS is introduced into practice in December 2001 (http://www.ibmh.msk.su/PASS). On the site, one can find a detailed description of the PASS approach as well as some examples of its applications, and estimate the quality of prediction by submitting structures of substances with known activities.
Current Medicinal Chemistry, 2003, 10 (3), 225-233.

The program PASS-BioTransfo is presented, which is capable of predicting many classes of biotransformation for chemical compounds. A particular class of biotransformation is defined by the chemical transformation type and may additionally include the name of the enzyme involved in a transformation. An evaluation of the approach is presented, using biotransformations taken from the databases Metabolite (MDL) and Metabolism (Accelrys), respectively. When trained with biotransformations from Metabolite, PASSBioTransfo predicts 1927 classes of biotransformation; the average accuracy estimated in LOO cross-validation is about 88%. After training with the biotransformations from the Metabolism database, 178 classes of biotransformation are predicted with an average accuracy of about 85%. The results of cross-prediction with several training and evaluation sets are presented and discussed.
Journal of Chemical Informrmation and Computer Sciences, 2003, 43 (5), 1636-1646.

A new approach is described that is able to predict the most probable metabolic sites on the basis of a statistical analysis of various metabolic transformations reported in the literature. The approach is applied to the prediction of aromatic hydroxylation sites for diverse sets of substrates. Training is performed using the aromatic hydroxylation reactions from the Metabolism database (Accelrys). Validation is carried out on heterogeneous sets of aromatic compounds reported in the Metabolite database (MDL). The average accuracy of prediction of experimentally observed hydroxylation sites estimated for 1552 substrates from Metabolite is 84.5%. The proposed approach is compared with two electronic models for P450 mediated aromatic hydroxylation: the oxenoid model using the atomic oxygen and the model using the methoxy radical as a model for the heme active oxygen species. For benzene derivatives, the proposed method is inferior to the oxenoid model and as accurate as the methoxy-radical model. For hetero- and polycyclic compounds, the oxenoid model is not applicable, and the statistical method is the most accurate. Broad applicability and high speed of calculations provide the basis for using the proposed statistical approach for high-throughput metabolism prediction in the early stages of drug discovery.
Journal of Chemical Information and Computer Sciences, 2004, 44 (6), 1998-2009.

Present review describes research on novel natural antitumor agents isolated from marine sponges. More than 90 novel cytotoxic antitumor compounds and their synthetic analogs have shown confirmed activity in vitro tumor cell lines bioassay and are of current interest to NCI for further in vivo evaluation. A great problem, to use directly the reservoir of marine organisms for therapy is the very low availability and the isolation of only very small amounts of the biologically active substances from the natural materials. Thus, the synthetic chemistry is required to develop high yield synthetic methods, which are able to produce sufficient marine alkaloids for a broad biological screening. This review will present some of the aspects of the medicinal chemistry developed recently to introduce such modifications. The structures, origins, synthesis and biological activity of a selection of N-heterocyclic marine sponge alkaloids are reviewed. The emphasis is on compounds poised as potential anticancer drugs: pyrroles, pyrazines, imidazole, and other structural families. With computer program PASS some additional biological activities are also predicted, which point toward new possible applications of these compounds. This review emphasizes the role of marine sponge alkaloids as an important source of leads for drug discovery.
Mini-Reviews in Medicinal Chemistry, 2005, 5 (3), 319-336.

The potential of the computer program PASS (Prediction Activity Spectra for Substances) to predict rodent carcinogenicity for chemical compounds was studied. PASS predicts carcinogenicity of chemical compounds on the basis of their structural formula and of structure–activity relationship analysis of known carcinogens and non-carcinogens. The data on structures and experimental results of 2-year carcinogenicity assays for 412 chemicals from the NTP (National Toxicological Program) and 1190 chemicals from the CPDB (Carcinogenic Potency Database) were used in our study. The predictions take into consideration information about species and sex of animals. For evaluation of the predictive accuracy we used two procedures: leave-one-out cross-validation (LOO CV) and leave-20%-out cross-validation. In the last case we randomly divided the studied data set 20 times into two subsets. The data from the first subset, containing 80% of the compounds, were added to the PASS training set (which includes about 46,000 compounds with about 1500 biological activity types collected during the last 20 years to predict biological activity spectra), the second subset with 20% of the compounds was used as an evaluation set. The mean accuracy of prediction calculated by LOO CV is about 73% for NTP compounds in the ‘equivocal’ category of carcinogenic activity and 80% for NTP compounds in the ‘evidence’ category of carcinogenicity. The mean accuracy of prediction for the CPDB database is 89.9% calculated by LOO CV and 63.4% calculated by leave-20%-out cross-validation. Influence of incorporation of species and sex data on the accuracy of carcinogenicity prediction was also investigated. It was shown that the accuracy was increased only for data on male animals.
Mutation Research, 2005, 586 (2), 138-146.

We propose a new approach to predict functional specificity of proteins from their amino acid sequences. Our approach is based on two things: structural Multilevel Neighborhoods of Atom (MNA) descriptors and an original Bayesian algorithm. Usually, a protein sequence is presented as a string of amino acid symbols. Here we introduce a new description of an amino acid sequence: a set of structural MNA descriptors. The MNA descriptor is a string describing an atom and its neighbor atoms according to the selected level. In this work, we also use description of a protein sequence as a set of peptides (strings of amino acid symbols). We performed a case study on two subsubclasses of enzyme nomenclature (EC). It is shown that B-statistics give a sufficient predictive power of enzyme specificity prediction for both MNA descriptors and peptides. We also showed that MNA descriptors give higher accuracy values in comparison with peptides and also provide a choice of MNA descriptor levels for best accuracy prediction. The highest average accuracy prediction that was achieved was 0.98.
OMICS: A Journal of Integrative Biology, 2006, 10 (1), 56-65.