QSAR of aminopyrido[2,3-d]pyrimidin-7-yl derivatives: Anticancer drug design by computed descriptors

Abstract

A series of aminopyrido[2,3-d]pyrimidin-7-yl derivatives acting as potential tyrosine kinase inhibitors having anticancer activities have been considered in the present investigation for the quantitative structure-activity relationship studies based on 2D and 3D QSAR approaches. For this purpose, various theoretical molecular descriptors were computed solely from the structures of these compounds. As the number of molecular descriptors greatly exceeds the number of observations, conventional regression does not produce reliable models and therefore, ridge regression methodology was used to solve this problem. The influence of different classes of molecular descriptors on the activity has been predicted and the most significant descriptors were obtained using the ridge regression models. Partial least squares (PLS) models were developed based on the training set for the 3D QSAR models of the above compounds. The influences of steric and electrostatic field effects generated by the contribution plots are discussed.

Keywords::

• QSAR
• ridge regression
• structural descriptors
• murine tumors
• pyrido pyrimidine derivatives
• anticancer drug design
• tyrosine kinase
• inhibition

Declaration of interest: Sisir Nandi thanks the Council of Scientific and Industrial Research, New Delhi 110001, India for the grant of a Senior Research Fellowship.