In silico study for discovering novel thiazole derivatives as anti-breast cancer agents (MCF-7)

Abstract

The work validated the pharmacokinetic characteristics and docking approach, and discovered novel thiazole compounds with MCF-7 breast anti-cancer efficacy using quantitative structure-activity relationships (QSAR) models. Using an experimental dataset of 53 thiazole derivatives with IC50 values, QSAR models were developed using multivariate linear regression (QSARMLR) and artificial neural networks (QSARANN). Eight descriptions, with statistical values of R2train = 0.875 and Q2LOO = 0.834, comprised the successfully developed QSARMLR model. Based on the descriptors of the QSARMLR model, which has statistical values of R2 = 0.918, Q2test = 0.934, and Q2CV = 0.916, the QSARANN neural network model with the architectural network of I(8)-HL(9)-O(1) has also been constructed. Using the AD and outliers analysis, the models were utilized to forecast 120 new design derivatives based on the thiazole framework, and the IC50 values of 10 new thiazole derivatives were determined. Additionally, the derivatives were assessed for resistance to estrogen-positive breast cancer by docking them onto the Polo-like kinases (Plk1) receptor and screening them for pharmacokinetic features in accordance with Lipinski and Ghose guidelines. Consequently, it was discovered that the thiazole TAZ5 had promising derivative activity against the primary MCF-7 breast cancer cell line.