Halogenated 2,4-diphenyl indeno[1,2-B]pyridinol derivatives as potential inhibitors of the androgen receptor (PDB ID: 58TE): A study of QSAR modeling, molecular docking, and pharmacokinetics for prostate cancer treatment
Year: 2024
Type: article
Abstract: The present research was centered on the development of a predictive model aimed at assessing the responses of prostate tumors to a range of compounds within the scope of the pIC 50 project. Our approach involved the utilization of a quantitative structure-activity relationship (QSAR) methodology, incorporating a genetic algorithm (GA) and multiple linear regression analyses (MLRA) applied to a dataset comprising 128 derivatives. The resultant model demonstrated a robust predictive capacity, supported by Remarkable internal and external validation metrics (R² = 0.9143 for internal consistency and R² ext . = 0.9523 for external validation), thus indicating its reliability in prognosis of tumor responses. Additionally, molecular docking simulations yielded valuable insights into the interactions between the ligands and target proteins, highlighting the significance of specific amino acid residues in the binding mechanism. These interactions were characterized by hydrogen bonds, halogen bonds, and pi-cation interactions, pointing to a substantial binding affinity with a score of −10.9 kJ/mol. The comprehensive molecular understanding obtained, including the critical role of ligand functional groups in determining interaction types, pointing out the utility of the model in identifying potential therapeutic agents. Furthermore, the study highlighted the modulation of the androgen receptor as an essential target in the treatment of prostate cancer. Molecular docking highlighted important interactions that can inform the design of novel anti-prostate cancer agents. Through the application of GA-MLR techniques, the study not only confirmed the efficacy of the predictive model in understanding prostate cancer responses but also showcased the potential of QSAR modeling in advancing prostate cancer research. The outcomes furnish a solid basis for further exploration of therapeutic compounds, emphasizing the intricate interplay between molecular structure and biological activity in the domain of drug development.
Source: Biomedical Analysis
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402560021