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<p>This study provides the first assessment of the biological activity (pIC<sub>50</sub>) of a group of isonicotinamides derivatives as inhibitors of Glycogen synthase kinase-3 beta (GSK3β) in Alzheimer's disease. The analysis has been carried out by developing appropriate quantum descriptors using density functional theory. The study focuses on investigating the interactions between inhibitors and the biomolecule Asparagine. This is done by analyzing the total energy (E), electronegativity (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\chi\)</EquationSource> </InlineEquation>), and electron transfer (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\Delta N\)</EquationSource> </InlineEquation>) with the amino acid of the host protein GSK3β. These descriptors are found to be highly promising and can explain 91% of the experimental biological activity. The most favourable regression model, derived from a training set consisting of 25 potential candidates, is also utilised to assess a set of similar chemicals. The studied quantum chemical descriptors are determined to be the most accurate representation of the biological activity of isonicotinamides within the context of drug development's quantitative structure–activity relationship (QSAR).</p> Graphical abstract <p></p>

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Quantitative structure–activity relationship (QSAR) investigation of isonicotinamide derivatives as GSK-3β inhibitors using DFT based quantum descriptors for Alzheimer’s disease

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Sarthak J. Trivedi,
  • Sutapa Mondal Roy,
  • Debesh R. Roy

摘要

This study provides the first assessment of the biological activity (pIC50) of a group of isonicotinamides derivatives as inhibitors of Glycogen synthase kinase-3 beta (GSK3β) in Alzheimer's disease. The analysis has been carried out by developing appropriate quantum descriptors using density functional theory. The study focuses on investigating the interactions between inhibitors and the biomolecule Asparagine. This is done by analyzing the total energy (E), electronegativity ( \(\chi\) ), and electron transfer ( \(\Delta N\) ) with the amino acid of the host protein GSK3β. These descriptors are found to be highly promising and can explain 91% of the experimental biological activity. The most favourable regression model, derived from a training set consisting of 25 potential candidates, is also utilised to assess a set of similar chemicals. The studied quantum chemical descriptors are determined to be the most accurate representation of the biological activity of isonicotinamides within the context of drug development's quantitative structure–activity relationship (QSAR).

Graphical abstract