Abstract
In the present study both classification and correlation techniques have been successfully employed for the development of the models of diverse nature for the prediction of melanocortin 4-receptor (MC4 R) agonist activity using a dataset comprising of 56 analogues of 4-substituted piperidine-4-ol derivatives. Decision tree (DT), random forest (RF), moving average analysis (MAA) and multiple linear regression (MLR) were utilised for development of the said models. The statistical significance of models was assessed through specificity, sensitivity, overall accuracy, Mathew's correlation coefficient (MCC) and intercorrelation analysis. High accuracy of prediction up to 98% was observed using these models. Proposed models offer vast potential for providing lead structures for the development of potent therapeutic agents for the treatment of male sexual dysfunction.
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