2,3,6-trisubstituted Quinazolin-4(3H)-one: Exploring Various Chemometric Tools and Artificial Neural Network (ANN) Techniques for Antitumor Activity

Title:2,3,6-trisubstituted Quinazolin-4(3H)-one: Exploring Various Chemometric Tools and Artificial Neural Network (ANN) Techniques for Antitumor Activity

Volume: 13 Issue: 7

Author(s): M. K. Kathiravan, S. S. Nilewar and A. N. Kale

Affiliation:

Keywords: QSAR, antitumor activity, FA-MLR, ANN, PCR.

Abstract: Quantitative structure activity relationship (QSAR) studies have been performed on the 27 molecules belonging to a series of 2,3,6-trisubstituted quinazolin-4(3H)-one derivatives for their antitumor activity. To explore the relationship between structure and activity, various chemometric tools have been employed such as Factor Analysis-Multiple Linear Regressions (FA-MLR), Artificial Neural Network (ANN) analysis and Principle Component Regression (PCR) methods. The models exhibited good correlation coefficient (r²) and cross validated correlation coefficient (q²) for all methods. It was found that ANN method gave better results indicating that the topological (IC4 and MPC06), constitutional (nf) and geometrical (G (N..S)) parameters were the most significant parameters for the antitumor activity indicating that novel potent analogs can be synthesized by altering these descriptor characteristics.

Cite this article as:

Kathiravan K. M., Nilewar S. S. and Kale N. A., 2,3,6-trisubstituted Quinazolin-4(3H)-one: Exploring Various Chemometric Tools and Artificial Neural Network (ANN) Techniques for Antitumor Activity, Letters in Drug Design & Discovery 2016; 13 (7) . https://dx.doi.org/10.2174/1570180812666150819002831