The review considers the current level of computer modelling of the relationship between structure of organic compounds and drugs and their ability to penetrate the blood brain barrier (BBB). All descriptors that influence BBB permeability within classification and regression QSAR models have been summarized and analyzed. Special attention is paid to the crucial role of H-bond for processes of both passive and active transport across the BBB. It is concluded that subsequent progress in computer modelling of the BBB penetration capacity for drug substances will be achieved after characterization of a spatial structure of the full size P-glycoprotein molecule with high resolution and the creation of QSAR models describing quantitative relationship between structure and active transport of substances across the BBB.
Biochemistry (Moscow) Supplement Series B: Biomedical Chemistry, 2013, 7 (2), 95–107.

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.

This article reviews the application of fragment descriptors at different stages of virtual screening: filtering, similarity search, and direct activity assessment using QSAR/QSPR models. Several case studies are considered. It is demonstrated that the power of fragment descriptors stems from their universality, very high computational efficiency, simplicity of interpretation and versatility.
Combinatorial Chemistry and High Throughput Screening, Volume 11, Issue 8, September 2008, Pages 661-668. DOI: 10.2174/138620708785739907

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.

A new QSAR approach based on a Quantitative Neighbourhoods of Atoms description of molecular structures and self-consistent regression was developed. Its prediction accuracy, advantages and limitations were analysed from three sets of published experimental data on acute toxicity: 56 phenylsulfonyl carboxylates for Vibrio fischeri; 65 aromatic compounds for the alga Chlorella vulgaris and 200 phenols for the ciliated protozoan Tetrahymena pyriformis. According to our findings, the proposed approach provides a good correlation and prediction accuracy (r2=0.908 and Q2=0.866) for the set of 56 phenylsulfonyl carboxylates and the 65 aromatic compounds tested on C. vulgaris (r2=0.885, Q2=0.849). For the 200 phenols tested on T. pyriformis, the prediction accuracy was r2¼0.685 and Q2¼0.651. This is at least as good as the best results obtained with the other QSAR methods originally used on the same data sets.
SAR & QSAR in Environmental Research, 2007, 18 (3-4), 285-298.

On the basis of computer prediction of biological activity by PASS and toxicity by DEREK, the most promising 32-alkylaminoacyl derivatives of 3-aminobenzo[d]isothiazole were selected for possible local anaesthetic action. This action was evaluated using an in vitro preparation of the isolated sciatic nerve of the rat and compared with lidocaine which was used as a reference compound. QSAR studies showed that the polarizability, polarity and molecular shape of molecules have a positive influence on their local anaesthetic activity, while contributions of aromatic CH and singly bonded nitrogen are negative. Since the estimated PASS probabilities to find local anaesthetic activity in the most active compounds are less than 50%, these compounds may be considered to be possible NCEs.
Eur. J. Med. Chem., 2009, 44 (2), 473-481.