Abstract
Background: With an aim to design a validated two‑dimensional quantitative structure–activity relationship (2D QSAR) model, a probe was executed on a series of reported c‑Jun NH2‑terminal kinase‑1 (JNK1) inhibitors, exhibiting selectivity toward JNKs (and not other members of MAPK family).
Objective: The present work focused on obtaining valuable insights from the structural architecture of the selected compounds and their effects on JNK1 inhibitory activity. The present work deciphers the importance of descriptive variables, namely Verloop L (Subst. 1), Bond Dipole Moment (Subst. 2), LogP (Subst. 1), Balaban Topological index (Subst. 1), and VAMP Total Dipole (whole molecule), in molecules possessing JNK1 inhibitory profile.
Results: These explanatory variables, obtained after iteratively reducing the data, did not only provide us with the substantial evidence pertaining to the dependence of bioactivity on the structural features of molecules, but also suggested the measures to optimize the selected compounds so as to obtain potent JNK1 inhibitors with good selectivity profile. Based on these distinct descriptors, exhibiting no apparent intercorrelation and manifesting good correlation with biological activity, a 2D QSAR model was generated.
Conclusion: Robustness of the developed model was evaluated by performing multiple linear regression, partial least square, and artificial neural network studies. The reliability and predictive ability of the developed model was ascertained through the values of standard statistical parameters, such as s = 0.38, F = 97.22, r = 0.95, r2 = 0.90, and r2cv = 0.88, for the training set compounds. The generated model was validated through the test set compounds, as well as by leave one out method.
Objective: The present work focused on obtaining valuable insights from the structural architecture of the selected compounds and their effects on JNK1 inhibitory activity. The present work deciphers the importance of descriptive variables, namely Verloop L (Subst. 1), Bond Dipole Moment (Subst. 2), LogP (Subst. 1), Balaban Topological index (Subst. 1), and VAMP Total Dipole (whole molecule), in molecules possessing JNK1 inhibitory profile.
Results: These explanatory variables, obtained after iteratively reducing the data, did not only provide us with the substantial evidence pertaining to the dependence of bioactivity on the structural features of molecules, but also suggested the measures to optimize the selected compounds so as to obtain potent JNK1 inhibitors with good selectivity profile. Based on these distinct descriptors, exhibiting no apparent intercorrelation and manifesting good correlation with biological activity, a 2D QSAR model was generated.
Conclusion: Robustness of the developed model was evaluated by performing multiple linear regression, partial least square, and artificial neural network studies. The reliability and predictive ability of the developed model was ascertained through the values of standard statistical parameters, such as s = 0.38, F = 97.22, r = 0.95, r2 = 0.90, and r2cv = 0.88, for the training set compounds. The generated model was validated through the test set compounds, as well as by leave one out method.
Keywords
Artificial neural network descriptors insulin receptor substrate multiple linear regression partial least square quantitative structure–activity relationship
Copyright
© 2019, Author(s). This open-access article is available under the Creative Commons Attribution 4.0 (CC BY 4.0) International License (https://creativecommons.org/licenses/by/4.0/), which allows for unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.