DISCOVERY OF NOVEL AND SELECTIVE C-JUN NH2-TERMINAL KINASES 2 INHIBITORS BY TWO-DIMENSIONAL QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP MODEL DEVELOPMENT, MOLECULAR DOCKING AND ABSORPTION, DISTRIBUTION, METABOLISM, ELIMINATION PREDICTION STUDIES: AN IN SILICO APPROACH
DOI:
https://doi.org/10.22159/ajpcr.2018.v11i5.24157Keywords:
QSAR, Multiple linear regression, Partial least square, Tanimoto index, Absorption, distribution, metabolism, and eliminationAbstract
Objective: Incited by the dearth of selective c-Jun NH2-terminal kinases (JNK) inhibitors, efforts have been made to design novel JNK2 inhibitors with good selectivity profile by utilizing a set of in silico tools.
Methods: The present study involved 2D QSAR model development through multiple linear regression and partial least square methods. Further, the information unveiled through the above model was meticulously utilized to design novel JNK2 inhibitors (compound a and compound b). The selectivity of the novel molecules was ascertained through the molecular docking experiments. Determination of Tanimoto similarity index and absorption, distribution, metabolism, and elimination (ADME) properties was performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.
Results: Four explanatory variables or descriptors, moment of inertia 2 length (subst.1), moment of inertia 3 length (subst.3), Kier Chi4 (path/ cluster) index (whole molecule), and vamp highest occupied molecular orbital (whole molecule), were found to possess profound influence on the biological activity with the values of standard parameters, s: 0.28, r: 0.96, r2: 0.92, r2CV: 0.82, and f=59.69. Tanimoto index values were found to be 0.095 and 0.075 for compound a and compound b, respectively.
Conclusion: The values of the statistical parameters proved the developed model to be of excellent quality. The results obtained for ADME prediction studies proved both the compounds to be better than the most potent compound (compound 45) of the set of JNK2 inhibitors selected for model development. In addition, extremely low Tanimoto similarity index values for both the compounds provided sufficient evidence for the novelty of the designed molecules.
Objective: Incited by the dearth of selective c-Jun NH2-terminal kinases (JNK) inhibitors, efforts have been made to design novel JNK2 inhibitors with good selectivity profile by utilizing a set of in silico tools.
Methods: The present study involved 2D QSAR model development through multiple linear regression and partial least square methods. Further, the information unveiled through the above model was meticulously utilized to design novel JNK2 inhibitors (compound a and compound b). The selectivity of the novel molecules was ascertained through the molecular docking experiments. Determination of Tanimoto similarity index and absorption, distribution, metabolism, and elimination (ADME) properties was performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.
Results: Four explanatory variables or descriptors, moment of inertia 2 length (subst.1), moment of inertia 3 length (subst.3), Kier Chi4 (path/ cluster) index (whole molecule), and vamp highest occupied molecular orbital (whole molecule), were found to possess profound influence on the biological activity with the values of standard parameters, s: 0.28, r: 0.96, r2: 0.92, r2CV: 0.82, and f=59.69. Tanimoto index values were found to be 0.095 and 0.075 for compound a and compound b, respectively.
Conclusion: The values of the statistical parameters proved the developed model to be of excellent quality. The results obtained for ADME prediction studies proved both the compounds to be better than the most potent compound (compound 45) of the set of JNK2 inhibitors selected for model development. In addition, extremely low Tanimoto similarity index values for both the compounds provided sufficient evidence for the novelty of the designed molecules.
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