Literature DB >> 22497469

Electrotopological state atom (E-state) index in drug design, QSAR, property prediction and toxicity assessment.

Kunal Roy1, Indrani Mitra.   

Abstract

Over the last two decades, a great deal of research has been oriented towards determination of correlation between molecular structures and a variety of responses exhibited by such molecules. Extensive attempts have been made to quantitatively determine the influence of structural fragments on the property profile of molecules through the development of quantitative structure-activity/property/toxicity relationship (QSAR/QSPR/QSTR) models based on regression analysis using different descriptors. Among all descriptors, the topological ones constitute an essential class encoding the crucial structural fragments governing the activity/property or toxicity data of the molecules. To better indicate the important topological features and molecular fragments mediating a particular response, Kier and Hall developed the electrotopological state atom (E-state) indices in the early 90s. The ability to encode the topology and electronic environment of molecular fragments in unison portrayed the E-state indices as an indispensable tool in the field of QSAR/QSPR/QSTR studies. This review looks back at different applications of E-state indices in the field of quantitative analysis of molecular properties as a function of their structures for diverse groups of molecules with vivid range of response parameters. The studies summarized here would help to understand potential of the E-state indices to identify the structural attributes responsible for various responses of the molecules. Although the present review includes most of the important researches carried out employing E-state parameters as the major group of descriptors over the last 15 years, the search is not exhaustive one. Apart from the studies reviewed here, several other researches have also been performed where the E-state indices have been engaged in association with several other descriptors to determine the influential molecular fragments for various endpoints.

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Year:  2012        PMID: 22497469     DOI: 10.2174/157340912800492366

Source DB:  PubMed          Journal:  Curr Comput Aided Drug Des        ISSN: 1573-4099            Impact factor:   1.606


  5 in total

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4.  Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease.

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Journal:  Mol Divers       Date:  2021-07-29       Impact factor: 2.943

5.  ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation.

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  5 in total

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