Int. J. Computational Biology and Drug Design, Vol. 11, No. 3, 2018 209 Copyright © 2018 Inderscience Enterprises Ltd. Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis Madhulata Kumari* Department of Information Technology, Kumaun University, SSJ Campus, Almora, Uttarakhand 263601, India Email: mchandra724@gmail.com *Corresponding author Neeraj Tiwari Department of Statistics, Kumaun University, SSJ Campus Almora, Uttarakhand, 263601, India Email: kumarn_amo@yahoo.com Subhash Chandra Department of Botany, Kumaun University, SSJ Campus, Almora, Uttarakhand 263601, India Email: scjnu@yahoo.co.in Naidu Subbarao School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India Email: nsrao@mail.jnu.ac.in Abstract: Machine learning techniques are advanced computational techniques which can be used to build the quantitative structure–activity relationship (QSAR) model of compounds dataset to find out important descriptors which are able to predict a specific biological activity from unknown compounds to discover better drugs. In the present study, by optimising descriptors using correlation-based feature selection, principal component analysis, and genetic programming technique, several machine learning techniques were used to build QSAR models on three different experimental datasets of InhA inhibitors. The best QSAR models were deployed on a dataset of 1450 approved drug