2-D QSAR model development for α-amino suberic acid derivatives as a novel anticancer agent

Abstract

Structure activity relationship has been reported for 34 compounds derived from α-amino suberic acid. Compounds in this class typically inhibit human histone deacetylase by hyperacetylation of histone protein in both normal and cancer cells induce expression of p21 and differentiate surviving cancer cells to a non-proliferating phenotype. In the present study, quantitative structure activity relationship study was performed on a series of novel α-amino suberic acid derivatives as inhibitors of histone deacetylase. Multiple linear regression analysis was performed to derive quantitative structure activity relationship models which were further evaluated internally as well as externally for the prediction of activity. The best quantitative structure activity relationship model for multiple linear relationship analysis was selected with a correlation coefficient (r 2) of 0.855, cross validation r 2 (CV) 0.809 and the standard error 0.287. Further partial least square and neural network analysis were also performed for partial least square analysis correlation coefficient (r 2) 0.876, CV r 2 0.756, standard error 0.287 for neural networking analysis R 2 of training set 0.861, R 2 of test set 0.661. All of them generated comparable results which prove that the model formed is sound and has good predictability.