Combinatorial protocol in multiple linear regression (CP-MLR) analysis

ABSTRACT Cyclin D dependent kinases namely CDK4 and CDK6 regulate entry into S phase of the cell cycle. These are emerging validated targets for anti-cancer drug discovery. A QSAR study has been carried out on the 4-thiazol-N-(pyridin-2-yl)pyrimidin-2-amine derivatives, which were reported as highly potent and selective inhibitors of CDK4 and CDK6, in terms of Dragon descriptors with the aim to establish the quantitative relationships between the reported activities and molecular descriptors unfolding the substitutional changes. In deriving QSAR models, combinatorial protocol in multiple linear regression (CP-MLR) approach was used. These models have accounted for nearly 79 and 83% variance in the CDK4 and CDK6 observed activities, respectively. The statistically validated QSAR models and the descriptors emerged in these models provided rationales to explain the CDK4 and CDK6 inhibitory activities of these congeners. PLS analysis has also corroborated the dominance of CP-MLR identified descriptors. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly. Keywords: QSAR, CDK4 and CDK6 inhibitors, Combinatorial protocol in multiple linear regression (CP-MLR) analysis, PLS, Dragon descriptors, 4-Thiazol-N-(pyridin-2-yl) pyrimidin-2-amines.

ABSTRACT The inhibition activity of (2S)-cyanopyrrolidine analogues for dipeptidyl peptidase IV has been quantitatively analyzed in terms of topological 0D-, 1D- and 2D-descriptors based on molecular graph theory. Statistically sound models have been obtained between the activity and various DRAGON descriptors through combinatorial protocol-multiple linear regression (CP-MLR) computational procedure. Amongst the large number of such derived models, the most significant ones have only been discussed to draw meaningful conclusions. Additionally the inhibition activity for DPP8 enzyme, reported for a limited number of such congeners, has also been correlated with such descriptors. From the final statistically significant models, it appeared that the mode of actions of titled compounds were different for DPP IV and DPP8 enzyme systems. Applicability domain analysis carried out for DPP IV inhibitors revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.