2D & 3D-QSAR Studies on a Series of Quinoline-Amino-piperidine Derivatives as Potent Mycobacterium DNA-Gyrase-B Inhibitors
DOI:
https://doi.org/10.37285/ijpsn.2023.16.3.5Abstract
Introduction: Mycobacterium tuberculosis is a familiar infectious bacillus that causes tuberculosis, which primarily affects the lungs and the spinal cord. To combat the growing difficulties in treating MTB, it is necessary to create safe medications with novel mechanisms of action.
Objective: To design and develop some novel quinolone-amino piperidine derivatives with potent mycobacterium DNAgyraseB inhibitory using the QSAR technique.
Methods: Multiple linear regression (MLR), partial least squares (PLS), and k-nearest neighbour molecular field analysis ((kNN-MFA) were utilised in the development of 2D and 3D-QSAR models, respectively; these models were then validated.
Results: The recently developed 2D-QSAR model can explain 85.07% (r2 = 0.8507) of the total variance incorporated into the training set. In addition, the model has an internal prediction capacity (q2) of 77.65% and an external prediction capacity (pred r2) of 83.64%, respectively. The F test confirms that the likelihood of the model failing is extremely low. The 3D-QSAR model explains the values of k (2), q2 = 0.5707, pred r2 = 0.7843, q2 se = 0.3167, and pred r2 se = 0.3111. This demonstrates that the QSAR equation obtained in that way is statistically significant and that the model has a predictive capacity of 78.43%.
Conclusion: The robustness of the developed 2D or 3D-QSAR models provides the necessary information and is expected to provide an excellent option for drug design.
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Keywords:
Mycobacterium tuberculosis, Quinoline, DNA Gyrase, QSAR, MLR, PLS, CNN
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