'3D-QSAR-based, pharmacophore modelling, virtual screening, and molecular docking studies for identification of hypoxia-inducible factor-1 inhibitor with potential bioactivity.

Iron overload is a primary cause of vital organ failure in patients with blood transfusion-dependent beta-thalassemia, and the hypoxia-inducible factor-1 α (HIF-1α) plays an important role in iron homeostasis pathway. HIF-1α modulation as a potential therapeutic target approach for iron chelation in hepatocyte cells. In this study, we used a 3D quantitative structure-activity relationship (QSAR) analysis to predict the inhibitory activity of HIF-1α inhibitors for iron chelation in liver cells. These feature descriptors were used to build a 3D-QSAR model, which was validated using Cost analysis and Fischer's randomization test. The model was used to virtually search the chemical compound libraries for potential inhibitor candidates with least inhibitory activity. The High-throughput Docking (Libdock) approach was used to dock large repositories of chemical molecules. Following Libdock score screening, the protein-ligand poses were docked using docking optimization (Cdocker) method. Binding energy were calculated for the protein-ligand poses of lowest -Cdocker Energy and -Cdocker Interaction. Further, side chain hopping method was used to generate lead novel ligand from best hit pose of ligand. Molecular dynamics simulation study to evaluate the lead novel ligand. Our study demonstrates the utility of 3D-QSAR pharmacophore screening in predicting the inhibitory activity for target. Inhibition strategy for iron chelation provides an alternative routes for reducing the iron content.