QSAR, ADMET, Molecular docking and Dynamic studies of natural products as potential inhibitors for Breast Cancer

Abstract

This study was conducted to support the ongoing search for novel molecules effective enough to treat breast cancer derived from Trigonella foenum-graecum, Curcuma longa L., and Atriplex Halimus. The study combined computational techniques and computer simulations to evaluate their efficacy, as well as Fourier transform infrared (FTIR) and ultraviolet/visible (UV/VIS) spectroscopy of plant extract. Quantitative structure-activity relationship (QSAR) modeling was used to predict bioactivity; with 2D_3D-QSAR models developed using multiple linear regression (MLR) and support vector regression (SVR). Comparative analysis revealed that MLR outperformed SVR, achieving an R² value of 0.93, an RMSE value of 0.163, and a high Q² value of 0.87, demonstrating superior predictive accuracy. Model predictions were validated using cross-validation. Molecular docking simulations evaluated the binding interactions with key breast cancer targets (HER2, CDK4, AKT1, and MCF-7), revealing strong affinities ranging from -7 to -11.4 kcal/mol. Furthermore, in conjunction with the density functional theory (DFT) method (B3LYP/6- 311G(d,p)), molecular docking identified two promising candidates, fenugreekine and diosgenin, derived from fenugreek, which demonstrated potent inhibition of all proteins compared to the FDA-approved drugs Ibrance and Capivasertib, respectively. ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis of diosgenin, curcumin, and arbutin demonstrated their pharmacokinetic affinity and safety, making them promising candidates for further experimental validation