Mapping dye pharmacophores by the Comparative Molecular Surface Analysis (CoMSA): application to heterocyclic monoazo dyes

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].…”

Quantitative structure–affinity relationship study of azo dyes for cellulose fibers by multiple linear regression and artificial neural network

“…), and the available experimental data are rather diverse. That is why theoretical methods for calculation of quantitative structure-property/activity relationships (QSPR/QSAR) are widely used to study these interactions [8][9][10][11][12][13][14]. The QSPR/QSAR approach is based on the assumption that the variation of the behavior of the compounds, as expressed by any measured physicochemical properties, can be correlated with numerical changes in structural features of all compounds [15][16][17][18][19].…”

“…In fact, validation is a crucial aspect of any QSPR/QSAR [20]. Based on the hypothesis of specific dye-fiber interactions and as an alternative to classical QSPR studies, Comparative Molecular Field Analysis (CoMFA) [8,11] and Comparative Molecular Surface Analysis (CoMSA) [12,13] were also used to predict dye adsorption properties. Schüürmann et al [8] established models for the azo dye-fiber affinities using CoMFA without external validation.…”

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).…”