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Comparison of MLR, Isotonic Regression and KNN Based QSAR Models for the Prediction of Inhibitory Activity of HDAC6 Inhibitors

Sandhya Vijayasarathy and Jhinuk Chatterjee
Department of Biotechnology, PES Institute of Technology, Bangalore, Karnataka, India
Abstract—Histone deacetylase 6 (HDAC6), a member of class II HDACs is considered as a drug target for cancer due to its chief contribution in oncogenic cell transformation. The aim of this study was to develop Quantitative Structure Activity Relationship (QSAR) models and evaluate its performance in predicting the inhibitory activity of various HDAC6 inhibitors. To achieve this, a dataset comprising of forty HDAC6 inhibitors were collected from PubChem database and were subjected to processes like Calculation of descriptors, Data pre-processing, Selection of relevant descriptors, QSAR modeling followed by evaluation of the developed models using various statistical parameters. Best results were obtained for k nearest neighbor based QSAR model having Correlation Coefficient (r) = 0.98, Squared Correlation Coefficient (r2) = 0.96, Mean Absolute Error (MAE) = 0.06 and Root Mean Squared Error (RMSE) = 0.15 thus indicating that, it is the best method for prediction of inhibitory activity of HDAC6 inhibitors. Further, crossvalidation procedures need to be performed and the model must be tested against a large dataset to authenticate its predictive accuracy.

Index Terms—QSAR, histone deacetylase, multiple linear regression, isotonic regression, k nearest neighbor

Cite: Sandhya Vijayasarathy and Jhinuk Chatterjee, "Comparison of MLR, Isotonic Regression and KNN Based QSAR Models for the Prediction of Inhibitory Activity of HDAC6 Inhibitors," International Journal of Life Sciences Biotechnology and Pharma Research, Vol. 4, No. 2, pp. 127-131, April 2015.
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