A new open-source python script for QSAR studyand its validation of cytotoxicity activity of thiazole analogues on MCF-7 cell line

This is the authors' abstract. We don't add key points for this paper.

Our present study aimed to working on trending machine learning approach with anew open-source data analysis python script for the discovery of anticancer lead via building the QSAR model by using 53 compounds of thiazole derivatives.Total of 82 CDK molecular descriptor were downloaded from “chemdes” web server and used for our study. After training the model, we checked the model performance via cross-validation of external test set. The generated QSAR model afforded the ordinary least squares (OLS) regression as R 2 = 0.542, F=8.773, and adjusted R 2 (Q2) =0.481, std. error = 0.061, reg.coef_ developed were of, -0.00064 (PC1), -0.07753 (PC2), -0.09078 (PC3), -0.08986 (PC4), 0.05044 (PC5), and reg.intercept_ of 4.79279 developed through statsmodels.formula module. The performance of test set prediction was done by multiple linear regression, support vector machine, and partial least square regression classifiers of sklearn module, whichgenerated the model score of 0.5424, 0.6422, and 0.6422, respectively. Hence, we conclude that the R 2 values(i.e. the model score) obtained using this script via three diverse algorithms were correlated well and there is not much difference between themand may be useful in the design of a similar group of thiazolederivatives as anticancer agents.