Predictive ability of physicochemical properties of antiemetic drugs using degree‐based entropies

Abstract

AbstractAntiemetic 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 , , , , and 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.