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Predictive Comparative QSAR Analysis Of As 5-Nitofuran-2-YL Derivatives Myco bacterium tuberculosis H37RV Inhibitors Bacterium Tuberculosis H37RV Inhibitors

机译:作为5-硝基呋喃-2-YL衍生物的预测性比较QsaR分析   myco细菌结核病H37RV抑制剂细菌结核H37RV   抑制剂

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摘要

Antitubercular activity of 5-nitrofuran-2-yl Derivatives series weresubjected to Quantitative Structure Activity Relationship (QSAR) Analysis withan effort to derive and understand a correlation between the biologicalactivity as response variable and different molecular descriptors asindependent variables. QSAR models are built using 40 molecular descriptordataset. Different statistical regression expressions were got using PartialLeast Squares (PLS),Multiple Linear Regression (MLR) and Principal ComponentRegression (PCR) techniques. The among these technique, Partial Least SquareRegression (PLS) technique has shown very promising result as compared to MLRtechnique A QSAR model was build by a training set of 30 molecules withcorrelation coefficient ($r^2$) of 0.8484, significant cross validatedcorrelation coefficient ($q^2$) is 0.0939, F test is 48.5187, ($r^2$) forexternal test set (pred$_r^2$) is -0.5604, coefficient of correlation ofpredicted data set (pred$_r^2se$) is 0.7252 and degree of freedom is 26 byPartial Least Squares Regression technique.
机译:对5-硝基呋喃-2-基衍生物系列的抗结核活性进行定量结构活性关系(QSAR)分析,以试图得出和理解作为响应变量的生物活性与作为独立变量的不同分子描述符之间的相关性。使用40个分子描述符数据集构建QSAR模型。使用偏最小二乘(PLS),多元线性回归(MLR)和主成分回归(PCR)技术获得了不同的统计回归表达式。在这些技术中,与MLR技术相比,偏最小二乘回归(PLS)技术显示出了非常有希望的结果。QSAR模型是由30个分子的训练集建立的,相关系数($ r ^ 2 $)为0.8484,交叉验证的显着相关系数( $ q ^ 2 $)是0.0939,F检验是48.5187,($ r ^ 2 $)外部检验集(pred $ _r ^ 2 $)是-0.5604,预测数据集的相关系数(pred $ _rr2se $)通过偏最小二乘回归技术可知,自由度为0.7252,自由度为26。

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