Comparative Molecular Surface Analysis (CoMSA) for Modeling Dye−Fiber Affinities of the Azo and Anthraquinone Dyes

Abstract

Despite recent investigations aimed at modeling 3D QSAR for dye molecules a controversy still exists: can a pharamacophore hypothesis be used for such purposes. In the present publication we reported on the application of the CoMSA method for modeling 3D QSAR of azo and anthraquinone dyes. We obtained very predictive models, which significantly outperform those reported in the previous CoMFA studies, especially for the azo dyes. Our results proved the previous conclusion that steric requirements are far less pronounced for the cellulose cavities than for the classical drug receptor. Moreover, our results indicate that all molecular surface segments are important for dye-fiber interactions, which also makes an important difference in relation to the classical drug pharmacophore. On the other hand, high predictivity of the CoMSA models indicates that a pharmacophore concept is suitable for the description of the dye-fiber interactions. However, this pharmacophore must substantially differ from the drug pharmacophore used for the illustration of the drug-receptor interactions. From a theoretical point of view dye-cellulose interactions can be an interesting case in which shape decides the activity rules not by the steric repulsion but as a cofactor determining the electrostatic potential distribution.

Continuous indicator fields: a novel universal type of molecular fields

“…Various 2D and 3D-QSPR models for Dl have been reported in literature [43,44]. Its 3D-QSPR modeling have mostly been performed in the framework of the pharmacophore concept [45][46][47][48][49][50][51] and using the hypothesis concerning the availability of specific binding sites inside cavities in the crystalline areas of cellulose. Several 3D-QSPR models for azo and anthraquinone vat dyes have been built using CoMFA [45][46][47][48][49] and CoMSA [50,51] methods.…”

“…Its 3D-QSPR modeling have mostly been performed in the framework of the pharmacophore concept [45][46][47][48][49][50][51] and using the hypothesis concerning the availability of specific binding sites inside cavities in the crystalline areas of cellulose. Several 3D-QSPR models for azo and anthraquinone vat dyes have been built using CoMFA [45][46][47][48][49] and CoMSA [50,51] methods. It has been shown that the affinity of anthraquinone vat dyes to cellulose is largely dominated by electrostatic and steric interactions, whereas the additions of molecular orbital (HOMO and LUMO) energies along with dipole moments to the set of descriptors does not vary the model performance [46].…”

“…It has been shown that the affinity of anthraquinone vat dyes to cellulose is largely dominated by electrostatic and steric interactions, whereas the additions of molecular orbital (HOMO and LUMO) energies along with dipole moments to the set of descriptors does not vary the model performance [46]. Unlike classical drug pharmacophore, it has been shown that all molecular segments are important for dye-cellulose interactions, while steric requirements are far less important for the cellulose cavities than for the classical drug receptors [50]. It has been suggested that molecular shape affects dye-cellulose interaction by determining the electrostatic potential distribution [50].…”

“…Unlike classical drug pharmacophore, it has been shown that all molecular segments are important for dye-cellulose interactions, while steric requirements are far less important for the cellulose cavities than for the classical drug receptors [50]. It has been suggested that molecular shape affects dye-cellulose interaction by determining the electrostatic potential distribution [50].…”

Self-organizing Neural Networks for Modeling Robust 3D and 4D QSAR: Application to Dihydrofolate Reductase Inhibitors

“…Our previous publications described possible applications of self-organizing neural networks for modeling 3D and 4D QSAR [9][10][11][12][13][14][15][16][17][18]. A self-organizing neural network is an unsupervised learning scheme consisting only of a single layer, usually two-dimensional rectangular or hexagonal grid of nodes (neurons).…”