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<p>This research aims to find the most potent GSK-3<i>β</i> inhibitors from a novel series of 2-substituted 3-oxindoles using computational methods. DFT approach with B3LYP/6-311 + + G (d, p) method was implemented to optimize the geometry of molecules. The electronic parameters were calculated to develop a QSAR model which was employed to predict the biological activity, using multiple linear regression (MLR), Random Forest (RF), and Support Vector Regression (SVR). Also, the variable importance technique was used to identify and select the four most critical features that significantly impact the pIC<sub>50</sub> value and greatly contribute to the development of the model. Among these, the RF regressor (<i>r2</i> and MSE for the train set is 0.83 and 0.16, and for the test set, 0.78 and 0.11) showed good predictive performance. Molecular docking studies revealed that compound 12 showed the most stable and favorable interactions amongst all other molecules within the active pocket of GSK-3<i>β</i> which were responsible for inhibitory activity. Moreover, molecular dynamics studies suggested that the ligand 12 formed a stable complex and showed favorable interactions within the active region of GSK-3<i>β</i> that lasted for the entire simulation time of 100 ns. This study suggested molecule 12 as the most promising therapeutic candidate for Alzheimer’s disease.</p>

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2-substituted 3-oxindoles as glycogen synthase kinase 3β inhibitors: insights from ML based QSAR, molecular docking, and dynamics simulations

  • 期刊论文

  • 发表日期：

    2025年10月13日

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  • Samved Kulkarni,
  • Shubham Deshpande,
  • Mohit Shetty,
  • Priyanka Sahare,
  • Vidya Shinde,
  • Ashwini Patil,
  • Vinayak Walhekar,
  • Shankar G. Alegaon,
  • Shriram D. Ranade,
  • Sateesh Bandaru,
  • Ankit Ganeshpurkar,
  • Ravindra Kulkarni

摘要

This research aims to find the most potent GSK-3β inhibitors from a novel series of 2-substituted 3-oxindoles using computational methods. DFT approach with B3LYP/6-311 + + G (d, p) method was implemented to optimize the geometry of molecules. The electronic parameters were calculated to develop a QSAR model which was employed to predict the biological activity, using multiple linear regression (MLR), Random Forest (RF), and Support Vector Regression (SVR). Also, the variable importance technique was used to identify and select the four most critical features that significantly impact the pIC50 value and greatly contribute to the development of the model. Among these, the RF regressor (r2 and MSE for the train set is 0.83 and 0.16, and for the test set, 0.78 and 0.11) showed good predictive performance. Molecular docking studies revealed that compound 12 showed the most stable and favorable interactions amongst all other molecules within the active pocket of GSK-3β which were responsible for inhibitory activity. Moreover, molecular dynamics studies suggested that the ligand 12 formed a stable complex and showed favorable interactions within the active region of GSK-3β that lasted for the entire simulation time of 100 ns. This study suggested molecule 12 as the most promising therapeutic candidate for Alzheimer’s disease.