The inhibition of tumor angiogenesis has become a compelling approach in the development of anticancer drugs. In the present study, topological models were developed through decision tree and moving average analysis using a data set comprising 42 analogues of 3-aminoindazoles. A total of 22 descriptors (distance based, adjacency based, pendenticity and distance-cum-adjacency based) were used. The values of all 22 topological indices for each analogue in the dataset were computed using an in-house computer program. A decision tree was constructed for the receptor tyrosine kinase KDR (kinase insert domain receptor) inhibitory activity to determine the importance of topological indices. The decision tree learned the information from the input data with an accuracy of 88%. Three independent topological models were also developed for prediction of receptor tyrosine kinase inhibitory (KDR) activity using moving average analysis. The models developed were also found to be sensitive towards the prediction of other receptor tyrosine kinases i.e. FLT3 (fms-like tyrosine kinase-3) and cKIT inhibitory activity. The accuracy of classification of single index based models using moving average analysis was found to be 88%. The performance of models was assessed by calculating precision, sensitivity, overall accuracy and Mathew's correlation coefficient (MCC). The significance of the models was also assessed by intercorrelation analysis.
Keywords: 3-Aminoindazoles; Decision tree; Moving average analysis; Receptor tyrosine kinase inhibitors; Topological indices.