TITLE:
Quantitative Structure Activity Relationship Analysis of Selected Chalcone Derivatives as Mycobacterium tuberculosis Inhibitors
AUTHORS:
Alisi Ikechukwu Ogadimma, Uzairu Adamu
KEYWORDS:
Anti-Tuberculosis, Descriptors, GFA, Model Validation, QSAR
JOURNAL NAME:
Open Access Library Journal,
Vol.3 No.3,
March
14,
2016
ABSTRACT: 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). 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.
Subsequently, quantum chemical and molecular descriptors were generated and
divided into training and test sets by Kennard Stone algorithm. Internal and
external validations as well as Y-randomization tests were employed in model validation. Five predictive
models were generated by GFA. 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. Based on validation
results, model 4 was chosen as the best of the five models.