A. N. Zefirov

Prediction of rate constants of S N 2 reactions by the multicomponent QSPR method

299 One of the most important parameters of any reac� tion is its rate constant. However, when dealing with a chemical reaction, a researcher faces the necessity to consider the contributions of many components that form the reaction system: the solvent, substrate, and reagent. The reaction rate also dramatically depends on pressure and temperature. The problem of prediction of chemical reaction rate constants has long attracted the attention of researchers, and considerable progress has been achieved in this respect. For many reaction series, spe� cific models have been constructed that consider the effect of different reagent parameters. Such models are characterized by high correlation coefficients but can� not be thought of as universal [1, 2]. Therefore, it is of interest to construct a unified model that could predict, with an acceptable accuracy, the rate constants of nucleophilic substitution at a sat� urated carbon atom on the basis of the structures of all the reagents involved, no matter what class of com� pounds they belong to. Consideration of such a complicated problem from the standpoint of QSPR (quantitative structure–prop� erty relationship) methodology necessitates using the multicomponent QSPR (MQSPR) approach, which was previously successfully applied to predicting the solvation free energy, which depends on the structure of two substances, the solute and the solvent [3].

Multilevel approach to the prediction of properties of organic compounds in the framework of the QSAR/QSPR methodology

Nowadays, the development of methodology of constructing quantitative structure‐activity and structure‐property relationship (QSAR/QSPR) models aimed at improving the descriptor representation of chemical compounds and at applying increasingly sophisticated methods of analysis has achieved the saturation level when the available methods make it possible to extract from databases almost all information useful for prediction. As stated in [1], in most cases, the predictive power of models constructed with the use of “fairly good” sets of descriptors and fairly good methods of data processing depends only slightly on both the descriptor set and the method used and is nearly completely determined by the database used for constructing a model. Thus, further improvement of the descriptor representation of chemical compounds and the introduction of new machine-learning methods will lead only to little progress, whereas radically new ideas are required for the actual breakthrough in this direction to overcome the limitations caused by a lack of useful information in chemical databases.

Fragment descriptors in the QSPR method: Their use for calculating the enthalpies of vaporization of organic substances

The quantitative structure-property relationship (QSPR) method was used to study the enthalpy of vaporization at 25°C of 65 organic compounds representing 13 different classes. As an alternative to the dependence of the enthalpy of vaporization on the boiling temperature, a neural network QSPR model is suggested that allows this property to be predicted with the use of descriptors taking into account the fragment composition of molecules.

Molecular Modeling the Human A1 Adenosine Receptor and Study of the Mechanisms of Its Selective Ligand Binding

Adenosine receptors (classified as G-protein-coupled receptors) are present in the majority of human and mammalian cells and tissues and are involved in many key biological processes. Depending on their biochemical and pharmacologic properties, four subtypes of adenosine receptors (A1, A2a, A2b, and A3) are distinguished. All of them contain a typical transmembrane domain formed by seven α helices linked pairwise with three extracellular and three intracellular hydrophilic loops. The ligand-binding site of the receptor is located inside the transmembrane domain [1]. It is known that activation of the A1 and A3 receptors decreases the cAMP level, whereas activation of the A2a and A2b subtypes increases it. In addition, stimulation of A1 receptors causes activation of potassium channels and inhibition of calcium channels [2]. The receptors ligands of adenosine are widely used in pharmacology and medicine for treatment of some psychoneurological and cardiovascular diseases [3]. Although numerous agonists and antagonists of adenosine receptors are currently known [4], the majority of them do not exhibit sufficient selectivity and efficiency. In addition, no model of adenosine receptors containing not only the transmembrane α helices, but also the hydrophilic loops, has been designed thus far. In view of this, a thorough study of the structure of adenosine receptors and the mechanisms of ligand binding to the receptors, as well as the search for new highly selective and efficient ligands of these receptors is now a topical problem. The purpose of this work was to design a molecular model of the human A1 adenosine receptor and to study the mechanisms of selective binding of ligands to this receptor. Adenosine receptors are membrane proteins; they are difficult to crystallize and study by X-ray analysis. For this reason, the structure of these receptors is studied using molecular modeling based on homology with a template protein (usually, rhodopsin) that is also G-protein-coupled and that was studied by X-ray analysis [5]. To identify the amino acids forming each of the seven transmembrane α helices of the A1 receptor, we performed multiple alignment of the amino acid sequences of the four known subtypes of adenosine receptors and rhodopsin: TM1

Molecular modeling study of the mechanism of ligand binding to human melatonin receptors.

The epiphysis hormone melatonin plays a key role in the regulation of circadian rhythms of mammals, as well as in the functioning of the cardiovascular, immune, and digestive systems and retina [1]. The involvement of melatonin in the regulation of a great number of physiological processes has determined its use as a drug. However, when used as a drug, melatonin has certain disadvantages—a short degradation period and low solubility (which causes difficulties with preparation of the pharmaceutical dosage form), as well as a too broad spectrum of action. In view of this, the search for melatonin analogs that would be deprived of the above-listed disadvantages is very important. For this purpose, it is necessary to study the characteristic features of the structure of melatonin receptors and the main mechanisms of ligand‐receptor interactions. Melatonin receptors belong to the rhodopsin family of G protein-coupled receptors. Because the representatives of this family are located in the lipid bilayer of cell membrane, crystallization of these receptors and experimental study of their structure are hampered. Xray data were obtained for only one representative of this family, rhodopsin [2]. Since all receptors of the rhodopsin family have a similar structure, the most effective way of constructing molecular models of melatonin receptors is modeling based on their homology with rhodopsin. Two subtypes (Mel1a and Mel1b) of human melatonin receptors are known. Similar to other representatives of the rhodopsin family, the melatonin receptor consists of seven transmembrane α -helices, three intracellular and three extracellular hydrophilic loops, as well as an intracellular and an extracellular terminal domains.

Calculation of the Enthalpy of Sublimation by the QSPR Method with the Use of a Fragment Approach

The enthalpies of sublimation of organic compounds belonging to various classes were studied for the first time in terms of the fragment approach based on the QSPR (Quantitative Structure-Property Relationships) method. The applicability of this technique to calculation of this parameter was demonstrated and a model that makes it possible to predict the enthalpy of sublimation of compounds on the basis of descriptors taking into account the fragment composition of a molecule was suggested.

Fragmental descriptors in QSPR: application to molecular polarizability calculations

Applicability of the fragmental approach developed in the framework of the QSPR methodology to prediction of the molecular polarizability of various classes of organic compounds is demonstrated. The model proposed allows reliable prediction of the molecular polarizability of organic compounds based on their chemical composition and a set of fragmental descriptors, which characterize the multiple and aromatic bonds as well as fused aromatic systems.

On mechanism of allosteric modulation of NMDA receptor via amino-terminal domains

A possible mechanism of action of the allosteric modulators of NMDA (N-methyl-d-aspartate) receptors is proposed that involves the stabilization of the twisted closed-clamshell configuration of the amino-terminal domains of GluN1 and GluN2B subunits by negative modulators while positive modulators stabilize a roughly parallel tight arrangement of these domains. These respective motions may play an important role in the transition between the open-channel and closed-channel states of the receptor. In addition, some features of the negative modulator binding site found by means of the molecular dynamics study and pocket analysis can be used in the rational design of the allosteric NMDA receptor modulators.

Computer-aided design of negative allosteric modulators of NMDA receptor

22 The N methyl D aspartic acid (NMDA) receptor, along with kainic acid and amino 3 hydroxy 5 methyl 4 isoxazolepropionic acid (AMPA) receptors, belongs to the large family of glutamate gated ion channels involved in many important neurophysiolog ical processes in the central nervous system associated with fast synaptic excitation transmission, memory formation, etc. [1]. Hyperactivation of NMDA recep tor causes a number of pathological conditions, including various neurodegenerative diseases. For this reason, its antagonists and reversible ion channel blockers were shown to be effective neuroprotector agents, in particular, for the treatment of Alzheimer’s disease [2].

Pseudofragmental descriptors based on combinations of atomic properties for prediction of physical properties of polymers in quantitative structure—property relationship studies

Among a great number of QSPR/QSAR approaches to the prediction of physical and chemical properties and biological activity of organic com� pounds, the methods using fragmental descriptors play a specific role [1, 2]. The values of the latter can be either the occurrence numbers or indicators of the presence of some fragments in the structures of chem� ical compounds. Advantages of these descriptors are their transparent meaning and the possibility of fast automatic generation on the basis of only the struc� tural formula. Fragmental descriptors can be calcu� lated without knowledge of the 3D structure or elec� tronic structure of molecules and, therefore, can be easily used for operating large databases. One of the disadvantages of fragmental descriptors is the problem of rare fragments that can be absent in the training set but can exist in the compounds for which the prediction is performed. Since the contribu� tions of rare fragments cannot be determined on the basis of the training set, considerable errors of predic� tion are expected for compounds containing such fragments. We suggest solving this problem by intro� ducing additional descriptors with the values being to an extent related to the contributions of fragments to the predicted property. For this purpose, we also sug� gest using special fragmental descriptors with the val� ues being calculated by combining the properties of the atoms that constitute these fragments. Such descriptors are referred to as pseudofragmental descriptors in order to distinguish them from “proper” descriptors assigned the values of the occurrence num� bers or indicators of the presence of certain fragments in the structures of chemical compounds. The atomic properties that are believed to influence the contribu� tions of fragmental descriptors to the predicted prop� erty, for example, the atomic weight, number of elec� trons, covalent radius, electronegativity, ionization potential, etc., can be used for predicting physical and chemical properties of organic molecules. It is also important for the used combinations of properties to have a clear physical meaning since this provides a bet� ter chance for the existence of correlation of their val� ues with fragmental contributions. If such a correla� tion exists, a small number of pseudofragmental descriptors enter into statistical models instead of numerous proper fragmental descriptors, including potentially rare, thus acting as a compressed generali� zation of the latter. This largely solves the problem of rare fragments if the pseudofragmental descriptors are constructed on the basis of frequently encountered fragments consisting of separate atoms or short chains of arbitrary atoms, which are present almost in all molecules.