Models for anti-tumor activity of bisphosphonates using refined topochemical descriptors

Abstract

An in silico approach comprising of decision tree (DT), random forest (RF) and moving average analysis (MAA) was successfully employed for development of models for prediction of anti-tumor activity of bisphosphonates. A dataset consisting of 65 analogues of both nitrogen-containing and non-nitrogen-containing bisphosphonates was selected for the present study. Four refinements of eccentric distance sum topochemical index termed as augmented eccentric distance sum topochemical indices 1-4 [formula: see text] have been proposed so as to significantly augment discriminating power. Proposed topological indices (TIs) along with the exiting TIs (>1,400) were subsequently utilized for development of models for prediction of anti-tumor activity of bisphosphonates. A total of 43 descriptors of diverse nature, from a large pool of molecular descriptors, calculated through E-Dragon software (version 1.0) and an in-house computer program were selected for development of suitable models by employing DT, RF and MAA. DT identified two TIs as most important and classified the analogues of the dataset with an accuracy of 97% in training set and 90.7% in tenfold cross-validated set. Random forest correctly classified the analogues with an accuracy of 89.2%. Four independent models developed through MAA predicted the activity of analogues of the dataset with an accuracy of 87.6% to 89%. The statistical significance of proposed models was assessed through intercorrelation analysis, specificity, sensitivity and Matthew's correlation coefficient. The proposed models offer a vast potential for providing lead structures for development of potent anti-tumor agents for treatment of cancer that has spread to the bone.