TITLE:
2D-QSAR Study of a Series of Pyrazoline-Based Anti-Tubercular Agents Using Genetic Function Approximation
AUTHORS:
Hemal M. Soni, Popatbhai K. Patel, Mahesh T. Chhabria, Dharmraj N. Rana, Bhushan M. Mahajan, Pathik S. Brahmkshatriya
KEYWORDS:
QSAR, Genetic Function Approximation, Descriptors, Cross-Validation
JOURNAL NAME:
Computational Chemistry,
Vol.3 No.4,
October
30,
2015
ABSTRACT: A series of pyrazoline-based new heterocycles have recently been synthesized from our group where some of the compounds display potent anti-tubercular activity against Mycobacterium tuberculosis H37Rv. In order to further explore the potency of the compounds, quantitative structure activity relationship study is carried out using genetic function approximation. Statistically significant (r2 = 0.85) and predictive (r2pred=0.89 and r2m=0.74)QSAR models are developed. It is evident from the QSAR study that majority of the anti-tubercular activity is found to be driven by lipophilicity. Also, molecular solubility, Jurs and shadow descriptors influence the biological activity significantly. Also, positive contribution of molecular shadow descriptors suggests that molecules with bulkier substituents are more likely to enhance anti-tubercular activity. Since the developed QSAR models are found to be statistically significant and predictive, they potentially can be applied for predicting anti-tubercular activity of new molecules for prioritization of molecules for synthesis.