QSAR, docking and pharmacokinetic studies of ۲,۴-diphenyl indenol [۱,۲-B] pyridinol derivatives targeting breast cancer receptors

This study presents a computational approach for designing potent compounds against breast cancer. A robust quantitative structure-activity relationship (QSAR) model, developed using genetic algorithms and multilinear regression analysis, predicts chemical activity (pGI۵۰) against breast cancer receptors. The model's reliability is validated with external metrics, emphasizing precision and strong relationships. Molecular docking investigations explore interactions between ۲,۴-diphenyl indenol [۱,۲-b] pyridinol derivatives and breast cancer receptors (۲RMJ, ۴OAR, ۴RDH, ۳ERT). Remarkable binding patterns are observed, insinuating at potential DNA gyrase inhibition. The compound's molecular properties and descriptors offer valuable insights into physicochemical characteristics, druglikeness, and potential pharmacological behavior. These findings contribute to drug design and development for personalized breast cancer therapy.This research integrates computational methodologies with experimental data, paving the way for effective and targeted breast cancer treatments. The study emphasizes the potential of computational analysis to enhance precision and efficacy in breast cancer treatment strategies.

کلیدواژه ها:

Breast cancer (BC) ، Protein Data Bank ، molecular descriptor ، Lee-Yang-Parr hybrid (B۳LYP) ، Concordant correlation coefficient (CCC)