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Predictive ability of physiochemical properties of benzene derivatives using Ve-degree of end vertices-based entropy

Topological indices relate chemical structure to chemical reactivity, physical properties, and biological activity. Quantitative structure-activity relationships (QSPR) are mathematical models proposed for the correlation of various types of chemical reactivity, biological activity, and physical pro...

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Published in:Journal of biomolecular structure & dynamics 2024-12, Vol.42 (22), p.12342-12352
Main Authors: Naeem, Muhammad, Rauf, Abdul, Mumtaz, Muhammad Waleed, Ameen, Nimra
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Language:English
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description Topological indices relate chemical structure to chemical reactivity, physical properties, and biological activity. Quantitative structure-activity relationships (QSPR) are mathematical models proposed for the correlation of various types of chemical reactivity, biological activity, and physical properties with topological indices/entropies. In this article, we have proposed the QSPR between the ve-degree of end vertices of edge based entropies and the physiochemical properties of benzene derivatives. We have designed a Maple-based algorithm for the computation of entropies. The relationship was analyzed using SPSS. We have shown that the physiochemical properties such as critical pressure, Henry's law, critical temperature, Gibb's energy, logP, critical volume, and molar refractivity can be predicted by entropies. All the results were highly positive and significant. The Randić, Balaban, and redefined third Zagreb entropies showed the best relations with physiochemical properties. Communicated by Ramaswamy H. Sarma
doi_str_mv 10.1080/07391102.2023.2269419
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source Taylor and Francis Science and Technology Collection
subjects Algorithms
Benzene - chemistry
benzene derivatives
Benzene Derivatives - chemistry
Entropy
physiochemical properties
Quantitative Structure-Activity Relationship
Quantitative structure-property relationship
title Predictive ability of physiochemical properties of benzene derivatives using Ve-degree of end vertices-based entropy
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