The 2D QSAR Research Of Paclitaxel Analogues

Pharmacokinetics (ADME/T:Absorption, distribution, metabolism, excretion and toxicity) is the important method in contemporary drug design and drug screening. Evaluation method of drugs early ADME/T properties can significantly improve the success rate of drug development, reduce drugs development costs, reduce drug toxicity and side effects, and guide clinical medication. This paper mainly uses multiple linear regression and principal component analysis to study the QSAR of multi-drug analogues; finally we established the stronger reliable and predictable QSAR model. This study did not find in the related literature at home and abroad, and has some innovative.This article is mainly the following aspects:1,Regressed the 197 molecular indices by multivariate linear regression and principal component regression analysis methods and finally got the best predictable mathematic models of their own. The model built by this method showed satisfactory statistical results whose proper predictability was validated by the independent test set (training set: R2=0.846, test set:R2=0.841). The key descriptors were identified, which are valuable and helpful for further researching and development of new paclitaxel analogues drugs.2,Regressed the 195 molecular indices by multivariate linear regression and principal component regression analysis methods and finally got the best predictable mathematic models of their own. The model built by this method showed satisfactory statistical results, whose proper predictability was validated by the independent test set (training set:R=0.782, test set:R=0.764). The key descriptors were identified, which are valuable and helpful for further researching and development of new paclitaxel analogues drugs.3,Regressed the 194 molecular indices by multivariate linear regression and principal component regression analysis methods and finally got the best predictable mathematic models of their own. From the analysis of the model, Stepwise regression analysis was found to be the optimal regression method compared with other multivariate linear regressions and principal component regression analysis. The model built by the stepwise showed satisfactory statistical results (R2=0.952, SEE=0.289), whose proper predictability was validated by the independent test set (R2=0.941, SEP=0.295). The key descriptors were identified, which are valuable and helpful for further researching and development of new CaM inhibitor drugs.