![]() The generated models showed that constitutional indices, 2D autocorrelations and radial distribution function (RDF) descriptors were important contributors to anti-tuberculosis activity of 1,3-diphenylprop-2-ene-1-one derivatives. ![]() Five predictive models were generated by GFA. Internal and external validations as well as Y-randomization tests were employed in model validation. Subsequently, quantum chemical and molecular descriptors were generated and divided into training and test sets by Kennard Stone algorithm. Geometry optimization was achieved at the density functional theory (DFT) level using Becke’s three-parameter Lee-Yang- Parr hybrid functional (B3LYP) in combination with the 6-31G* basis set. In order to gain further insights into the structural requirements for anti-tuberculosis activity by chalcone derivatives of 1,3-diphenylprop-2-ene-1-one, quantitative structure activity relationship (QSAR) was performed using genetic function approximation (GFA). Received 25 February 2016 accepted 10 March 2016 published 14 March 2016 This work is licensed under the Creative Commons Attribution International License (CC BY). 1Department of Applied Chemistry, Federal University, Dutsin-Ma, NigeriaĢDepartment of Chemistry, Ahmadu Bello University, Zaria, Nigeria
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