QSAR studies on some C14-urea tetrandrine compounds as potent anti-cancer against Leukemia cell line (K562)

Year 2018, Volume: 5 Issue: 3, 1387 - 1398, 01.09.2018

Abdullahi Mustapha , Gideon Shallangwa Muhammad Tukur Ibrahim , Abdullahi Umar Bello David Arthur Ebuka , Adamu Uzairu Paul Mamza

https://doi.org/10.18596/jotcsa.457618

Cited By: 10

Abstract

This research applied Quantitative Structure Activity Relationship (QSAR) technique in developing a Multiple-Linear Regression (MLR) model using Genetic Functional Algorithm (GFA) method in selecting relevant molecular descriptors from the structures of 24 C14-urea tetrandrine compounds. Firstly, the compounds were optimized at Density Functional Theory (DFT) level using Becke’s three-parameter Lee-Yang-Parr hybrid functional (B3LYP) with the 6-31G* basis set in the Spartan 14 Version 1.1.4 software. The molecular descriptors were calculated using Padel- software, and the results were divided in to training and test set. A model was built from the training set with internal validation parameter R2train as 0.910403. The external validation of the model was carried out using the test set compounds with validation parameter R2test as 0.6443 which passed the criteria for acceptability of a QSAR model globally. The coefficient of determination (𝑐𝑅2𝑝) parameter was calculated as 0.819296 which is greater than 0.5, this affirms that the generated model is robust. Furthermore, AST4p, GATS8v and MLFER are the descriptors in the model with positive mean effect of 0.089972855, 0.909814859 and 0.000212286 respectively. This study inferred that there will be positive influence on the inhibitory concentrations when the each descriptor value increases

Keywords

QSAR, Mean Effect, Validation, Descriptors, Model

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