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A molecular model of the human A2B adenosine receptor containing seven transmembrane α helices connected by three intracellular and three extracellular hydrophilic loops had been constructed. A molecular docking of seven structurally diverse xanthine antagonists of the A2B receptor was performed, and the differences in their binding modes were investigated. The 1 ns molecular dynamics (MD) simulations of several obtained ligand−receptor complexes inserted into the phospholipid bilayer were carried out. The conformational changes of the A2B receptor occurring during MD simulations were explored, and the stable binding modes of the studied antagonists were determined. According to the models presented in this work, the involvement of the His251, Asn282, Ser92, Thr89, and some aromatic residues in ligand recognition was determined. The obtained binding modes of the A2B antagonists demonstrate good agreement with the site-directed mutagenesis data.

An approach for evaluation of binding selectivity was suggested and exemplified using glycine/NMDA and AMPA receptors. For analyzing the pairwise selectivity, we propose to use the difference between biological activities (expressed as −log Ki) of ligands with respect to different receptor subtypes as a dependent variable for building comparative molecular field analysis (CoMFA) models. The resulting fields (which will be referred to as the “selectivity fields”) indicate the ways of increasing selectivity of binding, inhibition, etc. As an example, CoMFA of a set of pyrazolo[1,5-c]quinazolines and triazolo[1,5-c]quinazolines was used for considering the binding selectivity with respect to glycine/NMDA and AMPA receptors. In addition, the mapping of these fields onto the molecular models of the corresponding receptors makes it possible to reveal the reasons for experimentally observed selectivity as well as to suggest additional ways of increasing selectivity.

Homology modeling was used to build 3D models of the N-methyl-d-aspartate (NMDA) receptor glycine binding site on the basis of an X-ray structure of the water-soluble AMPA-sensitive receptor. The docking of agonists and antagonists to these models was used to reveal binding modes of ligands and to explain known structure−activity relationships. Two types of quantitative models, 3D-QSAR/CoMFA and a regression model based on docking energies, were built for antagonists (derivatives of 4-hydroxy-2-quinolone, quinoxaline-2,3-dione, and related compounds). The CoMFA steric and electrostatic maps were superimposed on the homology-based model, and a close correspondence was marked. The derived computational models have permitted the evaluation of the structural features crucial for high glycine binding site affinity and are important for the design of new ligands.

Methodological problems of using fragmental descriptors for construction of QSAR/QSPR equations are considered, and the main achievements in this field are summarized and discussed. If a structure−property data set is sufficiently large to allow building statistically significant models, then any topological index can be replaced with a set of fragmental descriptors. Several examples of using the fragmental approach for predicting retention indices and the normal boiling points of organic compounds are considered. Advantages of using fragmental descriptors, namely a “transparency” and interpretability of QSAR/QSPR models, are exemplified.

We present new homology-based models of the glutamate binding site (in closed and open forms) of the NMDA receptor NR2B subunit derived from X-ray structures of the water soluble AMPA sensitive glutamate receptor. The models were used for revealing binding modes of agonists and competitive antagonists, as well as for rationalizing known experimental facts concerning structure−activity relationships:  (i) the switching between the agonist and the antagonist modes of action upon lengthening the chain between the distal acidic group and the amino acid moiety, (ii) the preference for the methyl group attached to the α-amino group of ligands, (iii) the preference for the d-configuration of agonists and antagonists, and (iv) the existence of “superacidic” agonists.

Our investigation was motivated by a recent paper concerning “high-quality structure-property-activity regressions” (J. Chem. Inf. Comput. Sci. 2000, 40, 899−905) for boiling points of small sulfides using variable connectivity indices. We performed QSAR study of the same data set using a logically preselected solvation index and obtained a very good one-parameter regression. The structures of the whole possible set of small sulfides (C2−C6) were generated and the statistics were proven by real predictionusing an external test set of sulfides. The variants of extended prediction with extrapolated data and QSAR using an expanded training set were also performed, and all these data also revealed the preference of the solvation index. A general problem of descriptivevs predictiveQSAR is discussed.

Our investigation was motivated by a recent paper concerning “high-quality structure-property-activity regressions” (J. Chem. Inf. Comput. Sci. 2000, 40, 899−905) for boiling points of small sulfides using variable connectivity indices. We performed QSAR study of the same data set using a logically preselected solvation index and obtained a very good one-parameter regression. The structures of the whole possible set of small sulfides (C2−C6) were generated and the statistics were proven by real prediction using an external test set of sulfides. The variants of extended prediction with extrapolated data and QSAR using an expanded training set were also performed, and all these data also revealed the preference of the solvation index. A general problem of descriptive vs predictive QSAR is discussed.

A new method of QSAR analysis for organic compounds, molecular field topology analysis (MFTA), is considered that involves the topological superposition of the training set structures and the construction of a molecular supergraph (MSG). This enables the creation of the uniform descriptor vectors based on the local physicochemical parameters (atom and bond properties) of the molecules. The application of this technique is illustrated by a number of examples, and its features are discussed. The MFTA is especially suitable for solving the problems where the analysis of three-dimensional structure is either unnecessary or complicated.

The elaboration of methods for defining molecular similarity measures is one of the important fields of modern theoretical chemistry. These measures are used for solving a number of problems of theoretical and computer chemistry, in particular the prediction of properties of chemical compounds. For the construction of any molecular similarity measures molecules are represented as some mathematical objects {M}, on which quantitative similarity measures d(M1, M2) (M1, M2 ∈ {M}) are introduced. The most widely used way of molecular representation is based on picturing molecules as labeled graphs, labels of which encode types of atoms and bonds. There are many different similarity measures defined for graphs, expressed in terms of vectors of graph invariant, sequences, and sets derived from graphs or in terms of maximal common subgraph, etc. In general, there exists an infinite number of graph similarity measures. In the present paper an analytical description of the set of symmetric similarity measures defined for arbitrary labeled graphs is given. The found general formula for the measure depends on a number of parameters satisfying some conditions. Any particular graph similarity measure may be obtained from this formula at definite values of parameters.

The methods of quantitative conformational analysis of cyclic fragments in molecules and the computer program RICON (RIng CONformations) developed by us for this purpose are considered. Program RICON uses atomic coordinates obtained from X-ray studies or force field/quantum chemical computations and allows one to analyze geometric parameters of a molecule, to compute the puckering parameters of rings in the molecule using various methods, and to obtain a verbal description of a ring conformation. The abilities of the program are described, and the examples of its application are given.

A scheme of a neural device intended for searching direct correlations between structures and properties of organic compounds without preliminary computation of molecular descriptors (that are invariant with respect to renumbering atoms in a molecule) is suggested. The invariance of a property with respect to renumbering atoms in a molecule is ensured by the architecture of the neural device, which is constructed by analogy with biological vision systems. A model software of the neural device was tested on several examples. The descriptive and predictive performances of the device are shown to be comparable and even overcome the performances of using molecular descriptors, such as topological indexes and substructural descriptors, especially for analyzing heterogeneous data sets including inorganic compounds. The neural device can be advantageously used in the cases when more traditional approaches fail to work or “good” molecular descriptors have not been devised yet.