Structural correspondence as a basis for modelling of the inhibitory activity of baker triazines

Krutius, S. V.; Кузьмин, В. Е.

doi:10.1007/bf00776325

Cited by 1 publication

(5 citation statements)

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“…These compounds were previously studied by methods of indirect 3D-QSAR analysis [22,23], whereby the supermolecules were constructed by superimposing three-dimensional models of the corresponding ligands until obtaining the best overlap of the van der Waals volumes, and the regression equations were obtained on the basis of various characteristics of these supermolecules. The sample set includes compounds with various types of internal rotational degrees of freedom, and is sufficiently representative [23] of many well-known Baker triazines.…”

Section: Resultsmentioning

confidence: 99%

“…molecular geometry of the fragments was established on the basis of calculations [23] and the data on model compounds [33]. It should be noted that our approach does not require a thorough analysis of conformations such as that involved in the "supermolecular" approaches [22,23].…”

Section: Resultsmentioning

confidence: 99%

“…On the contrary, increasing the optimum criterion to K0 = 0.100 allows, besides the useful data on the similarity of ligands, a large amount of rough optimum superimpositions, thus also decreasing the quality of the model. The correlation coefficients 0.92 and 0.94 (Table 3) seem to be quite satisfactory, the more so since similar values R = 0.94-0.96 [23] and 0.90 -0.99 [22] were obtained from a labor-consuming conformation analysis of the Baker triazines. Note also that the FP method, unlike those used in [21 -24], is applicable to structurally dissimilar molecules [27].…”

Section: Nosmentioning

confidence: 91%

“…In the variant of the FP method under consideration, it is inexpedient to consider lipophilicity as a regressor since this property is taken into account in the stage of searching for the optimum superimpositions and calculation of the optimum criterion (1). Note that the prognosis made in [22] for the inhibiting activity of six Baker triazines had a rather high uncertainty of SA--1.05. The weights of fragrnents Wjt (Table 5) are clearly related to the structures of inhibitors.…”

Section: Nosmentioning

confidence: 97%

“…Below we will outline a modification of the approach developed previously [26 -28], which is directed toward solving the problem of conformational lability. As an example, we will perform a QSAR analysis of the Baker triazines --dihydrofolate reductase inhibitors--and compare the results with those reported by other researchers [22,23].…”

mentioning

confidence: 93%

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The method of frontal polyhedra for conformationally-nonrigid molecules. Quantitative structure—Activity relationship in the series of baker triazines—Dihydrofolate reductase inhibitors

Khlebnikov

1997

Pharm Chem J

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There are many approaches to establishing the quantitative relationship between chemical structure and biological activity (QSAR) of substances. The two key steps, which are common for all these methods, consist in representing a given structure by a set of descriptors and constructing a mathematical model-the fimction of descriptors approximating variations of the activity. The chemical structure can be described in greater or less detail using specially introduced indicator variables, physicochemical constants of the substituents [I], and various topological indices [3][4][5]. As a rule, the mathematical model has the form of a regression equation, offering a very useful empirical relationship. The main difficulty of this classical approach is to properly select the regressors, providing for the "best" model, from a huge variety of known descriptors. The standard procedure of the stepby-step regression analysis has certain limitations of a statistical character [6,7]. There were attempts to obtain the best approximating functions using non-traditional methods [8-I0] and pattern recognition algorithms [1,2].In recent years, considerable effort has been devoted toward directly taking into account the three-dimensional (3D) structure of molecules using special databases [11,12] and computer programs [13 -15]. The 3D approaches offer the most explicit implementation of the well-known hypothesis that the biological effect arises due to a ligand-receptor interaction of the "key-lock" type. A systematic analysis of the published data allows the 3D-QSAR methods to be subdivided into the following major groups. The first group con-" sists of methods proceeding directly from the structure of the receptor [16,17]. This approach is usually hindered by the lack of data on the spatial structure of the receptors involved. In this case researchers may define a set of points in space (pseudoreceptor) on the basis of conformational analysis of the compounds (ligands) studied [18][19][20]. The structural correspondence between ligands and receptor is characterized by quantities reflecting the energy of the ligand-receptor in-I I. I. Polzunov Altai State Technical University, Bamaul, Russia. 147 teraction, which are used in constructing the mathematical QSAR model. Another group includes "indirect" methods of 3D-QSAR analysis, in which assessment of the fitting of ligands to receptors is replaced by determination of the similarity between ligands, involving a procedure of optimum matching of three-dimensional molecular models. The characteristics of the resulting "supermolecule" [21 -24] or lattice [25] constructed in this way serves as a basis for the subsequent description of relationships between the structure and biological activity. Ligands studied by methods of indirect 3D-QSAR analysis must have close shapes and dimensions, since only this will lead to a unique determination of the supermolecule. Representing the imaginary receptor by a set of points also implies certain spatial resemblance between the objects studied, al...

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Section: Resultsmentioning

confidence: 99%