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A topological index, also known as a connectedness index, is a numerical characteristic of chemical structure that is computed by using its molecular graph in theoretical chemistry. In quantitative structure‐activity relationships (QSARs), which relate a molecule's chemical structure to its physical and chemical properties, topological indices are employed. The graph entropies with topological indices were inspired by Shannon's entropy concept and became the information‐theoretic quantities for measuring the structural information of chemical graphs. The theory of graphs is useful in determining the relationship between specific properties of chemical structures using different graph entropy measures. Through these entropies, many physical and chemical characteristics, such as melting point, energy generation, Henry's Law, and molar mass of chemical compounds, can be calculated. For this, the quantitative structure‐property relationship (QSPR) models are designed using ve‐degree‐based entropies to examine some physical properties of benzene derivatives. For the computation of entropies, a Maple‐based program is developed. The QSPR study is performed using SPSS and linear regression techniques. In this work, we have observed that the redefined third Zagreb entropy, the Balaban entropy, and the Randic entropy are the best predictors. The physiochemical characteristics, namely Henry's Law and critical pressure, can be predicted by Randic entropy and Balaban entropy, respectively. The redefined third Zagreb entropy can predict four other properties: enthalpy, molar mass, π‐electronic energy, and molecular weight. [ABSTRACT FROM AUTHOR]

Antiemetic drugs are prescribed to help with nausea and vomiting, which are side effects of other drugs. Topological indices/Entropies are used in QSPR research to predict the bioactivity of chemical substances. This paper proposes predicting physical properties using degree‐based entropies. A Maple‐based program is being developed to make the computation of degree‐based entropy easier. A QSPR analysis is an effective statistical tool for determining pharmacological activity or binding mode for various receptors. Using a linear regression model, we found that the Augmented Zagreb entropy helps predict Complexity and the first Zagreb entropy and Balaban entropy help predict Heavy Atom Count, Topological Polar Surface Area, Monoisotopic Mass and Molecular Weight. In multiple linear regression, the results exhibit that the ℰR−1, ℰABC, ℰF, ℰJ, and ℰReZG3 entropies statistically significantly predict the Heavy Atom Count, Topological Polar Surface Area, Complexity, Monoisotopic Mass & Molecular Weight. This analysis may help chemists and other working in the pharmaceutical industry predict the properties of antiemetic drugs without experimenting. [ABSTRACT FROM AUTHOR]