Traditional topological indexes vs electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research

Abstract

A comparison of the performance of molecular descriptors of different types was conducted. The study was concentrated on determining which descriptors are included in the best linear multivariate regression models constructed for modeling various physicochemical properties (melting point, boiling point, refractive index, molar volume, and density) and biological activities (anaesthetic activity, narcotic activity, sweetness intensity). A total of 84 molecular descriptors published over the past 2 decades was included in this study, plus some of their normalized and squared forms. It was shown that, for the estimation of physicochemical properties, the best small regression models with one to four parameters are mainly comprised of "classical" topological indices such as the Randić index, Wiener index, and molecular connectivity indices. For the correlation of biological activity, combinations of topological indices with geometrical descriptors produced regression models of the best quality. In-house developed software was used for generation of the molecular descriptors (the GROUND program) and for the statistical QSAR/QSPR analysis (the GROUNDSTAT program).

Classification of 5-HT1A receptor agonists and antagonists using GA-SVM method

“…Molecular descriptors are generally used for quantitative representation of the structural and physicochemical features of compounds [52,53] . Depending on the 3D structure of each compound, 292 molecular descriptors including topological, graph-theoretical, quantum-chemical and electro-topological state (E-state) descriptors were calculated using Discovery Studio 2.1 [54] .…”

A knowledge‐based approach for screening chemical structures withinde novomolecular evolution

“…There are also 3D-Molecule Representation of Structures based on electron diffraction (3D-MoRSE) [11], which are based on theoretical scattering curves calculated from the same transforms as used in electron diffraction studies. In addition, there are many topological indices [12][13][14][15] which may be applicable as well.…”

Proposição, validação e análise dos modelos que correlacionam estrutura química e atividade biológica

“…Essas variáveis devem abranger ampla gama de propriedades (lipofílica, eletrônica, estérea e polar). Além das constantes de substituintes utilizadas em QSAR clássi-co 7,8,[29][30][31][32][33] , devem-se incluir na análise propriedades físico-quí-micas moleculares tais como área superficial e volume moleculares 34 , propriedades derivadas de cálculo de orbital molecular 9, 35-37 , variáveis indicadoras 7,8,38 , índices de similaridade [39][40][41][42] e índices topológicos 34 . A utilização de grandes conjuntos de dados em QSAR pressupõe a necessidade de algum tipo de método de seleção de variáveis, como por exemplo, a busca sistemática 43 , as redes neurais [44][45][46][47][48][49][50] , os algoritmos genéticos e evolucionários 43,45,[51][52][53][54][55][56] e os métodos multivariados 25,45,52,[57][58][59][60] .…”