Models for Prediction of ^V600EBRAF and Melanoma Cells Growth Inhibitory Activities of Pyridoimidazolones

Abstract

Targeted inhibition of activated BRAF mutation has emerged as a most promising and putative therapeutic approach for the anticancer drug development. In the present study, an in-silico approach using decision tree and moving average analysis has been applied to a data set comprising of 43 analogues of pyridoimidazolones for development of models for prediction of both (V)600(E)BRAF and melanoma cells (BRAF WM266.4) growth inhibitory activities. A decision tree was mainly employed for determining the importance of molecular descriptors (n=46). The value of majority of these descriptors for each analogue in the dataset was computed using E-Dragon software (version 1.0). The decision tree learned the information from the input data with an accuracy of 98% and correctly predicted the cross-validated (10-fold) data with accuracy up to 79%. A total of three non-correlating descriptors, identified best by the decision tree analysis, were subsequently utilized for development of suitable models using moving average analysis. These proposed models resulted in the prediction of (V)600(E)BRAF inhibitory activity (IC50) and melanoma cells growth (SRB GI50) inhibitory activity with an overall accuracy of ≥90%. The statistical significance of models/descriptors was assessed through intercorrelation analysis, sensitivity, specificity and Matthew's correlation coefficient.