Integrated Computational Drug Discovery of Diosgenin‐Based Analogues as PI3K‐Targeted Breast Cancer Inhibitors: QSAR, Molecular Docking, and Molecular Dynamics Insights

Abstract

Dysregulation of the phosphoinositide 3‐kinase (PI3K) signalling pathway plays a critical role in breast cancer progression, therapy resistance, and poor clinical outcomes, highlighting the need for novel PI3K‐targeted therapeutic agents. This investigation utilized an integrated computational drug discovery framework to assess diosgenin‐derived compounds as potential PI3K inhibitors in breast cancer. Quantitative structure–activity relationship (QSAR) modelling was performed on 46 diosgenin derivatives, yielding a multiple linear regression model with a coefficient of determination (R 2 ) of 0.8138. Molecular docking targeting the breast cancer‐related PI3K receptor (PDB ID: 8TSB) identified favorable predicted binding scores for several compounds. SD_78 was prioritized as a promising candidate, with a docking score of −7.606 kcal/mol and an MM/GBSA binding free energy of −44.19 kcal/mol. Molecular dynamics (MD) simulations, supported by principal component analysis (PCA), free energy landscape (FEL), probability density function (PDF), and dynamic cross‐correlation matrix (DCCM) analyses, suggested sustained interactions and favorable dynamic behavior of the SD_78–PI3K complex. Density functional theory (DFT) calculations at the B3LYP/6‐311++G(d,p) level provided insights into the electronic properties of selected compounds. Collectively, these findings suggest that SD_78 and other diosgenin‐based derivatives may represent promising candidates for further experimental evaluation as PI3K‐targeted anticancer agents.