• Diverse models for the prediction of CDK4 inhibitory activity of substituted 4-aminomethylene isoquinoline-1, 3-diones

    • Fulltext


        Click here to view fulltext PDF

      Permanent link:

    • Keywords


      Decision tree; E-state contribution indices; augmented eccentric connectivity topochemical index; molecular connectivity index; connective eccentricity topochemical index.

    • Abstract


      In the present study, both classification and correlation approaches have been successfully employed for development of models for the prediction of CDK4 inhibitory activity using a dataset comprising of 52 analogues of 4-aminomethylene isoquinoline-1,3-(2𝐻,4𝐻)-dione. Decision tree, random forest, moving average analysis (MAA), multiple linear regression (MLR), partial least square regression (PLSR) and principal component regression (PCR) were used to develop models for prediction of CDK4 inhibitory activity. The statistical significance of models was assessed through specificity, sensitivity, overall accuracy, Mathew’s correlation coefficient (MCC), cross validated correlation coefficient, 𝐹 test, $r^2$ for external test set (pred_$r^2$), coefficient of correlation of predicted dataset (pred$_-$r2Se) and intercorrelation analysis. High accuracy of prediction offers proposed models a vast potential for providing lead structures for the development of potent therapeutic agents for CDK4 inhibition.

    • Author Affiliations


      Monika Gupta1 A K Madan2

      1. Faculty of Pharmaceutical Sciences, M D University, Rohtak 124 001, India
      2. Faculty of Pharmaceutical Sciences, Pandit B D Sharma University of Health Sciences, Rohtak 124 001, India
    • Dates

    • Supplementary Material

  • Journal of Chemical Sciences | News

© 2022-2023 Indian Academy of Sciences, Bengaluru.