A simple electrotopological index for quantitative structure-activity relationship correlation of physical properties with biomolecular activities

Abstract

Quantitative structure-activity relationship (QSAR) studies constitute a process by which the physicochemical properties of a set of chemical structures are quantitatively correlated with a measurable, such as the concentration of a substance required to give a certain therapeutic drug response. 2D-QSAR studies start with 10-20 analogues, ranging from biologically active to inactive; each analogue, regardless of bioactivity, is described by a series of descriptors. To further broaden the practical utility of these simple descriptors we have sought to identify hybrid indices which are trivial to calculate but which capture data from at least two categories of descriptors. An electrotopological descriptor, termed ET Z , which combines electronic information and molecular topology, has been devised and validated against a set of 25 anticonvulsant hydantoin molecules. This ET Z is based solely on atomic connectivity information obtained from the graph without explicit input from molecular geometry.Keywords Quantitative structure-activity relationship study · Descriptors · Topological index · Electrotopological index · Computer-aided drug design Quantitative structure-activity relationship (QSAR) studies constitute a process by which the physicochemical properties of a defined set of chemical structures are

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“…26 Descriptor based stochastic models are used in materials science to identify unreported, however, chemically plausible compounds that could have interesting properties, e.g., thermodynamical, 27 mechanical, 28 or electronic properties. 29 In high-trough-put screening they contribute to finding molecules with a desired biological activity and possible drugs (docking), 3,26,[30][31][32] or to build predictive models for in vivo toxicity analysis. 6 In chemoinformatics machine learning algorithms are popular to study reaction mechanisms of enzymes.…”

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“…1 These variables have been correlated in many QSAR studies applying various chemometric regression methods, as linear regression (LR), multiple linear regression (MLR), polynomial regression (PR), artificial neural networks (ANN), partial least squares regression (PLS), principal component regression (PCR), etc. [2][3][4][5][6][7][8] Any high-quality model obtained by aforementioned chemometric techniques may be used by the chemist in order to facilitate the synthesis of more effective drugs. A high-quality QSAR model must be based on reasonable number of tested compounds, characterized by good values of statistical parameters, defined for particular application domain and suitably validated by internal and external validation approaches.…”