Predictive Comparative QSAR analysis of Sulfathiazole Analogues as Mycobacterium Tuberculosis H37RV Inhabitors

TL;DR

This study develops QSAR models to predict antitubercular activity of Sulfathiazole derivatives, highlighting the effectiveness of PLS regression with high correlation coefficients and validation metrics.

Contribution

It introduces a QSAR modeling approach using PLS regression for Sulfathiazole analogues, demonstrating superior predictive performance over other methods.

Findings

PLS regression yielded a high correlation coefficient of 0.9191.

The QSAR model showed strong cross-validation with q^2 of 0.83.

External test set predictions indicated good model robustness.

Abstract

Antitubercular activity of Sulfathiazole Derivitives series were subjected to Quantitative Structure Activity Relationship (QSAR) Analysis with an attempt to derive and understand a correlation between the Biologically Activity as dependent variable and various descriptors as independent variables. QSAR models generated using 28 compounds. Several statistical regression expressions were obtained using Partial Least Squares (PLS) Regression, Multiple Linear Regression (MLR) and Principal Component Regression (PCR) methods. The among these methods, Partial Least Square Regression (PLS) method has shown very promising result as compare to other two methods. A QSAR model was generated by a training set of 18 molecules with correlation coefficient r (r square) of 0.9191, significant cross validated correlation coefficient (q square) of 0.8300, F test of 53.5783, r square for external test set pred_r square -3.6132, coefficient of correlation of predicted data set pred_r_se square 1.4859 and degree of freedom 14 by Partial Least Squares Regression Method.