Computational Drug Discovery of Herbal Phytoconstituents: Targeting Egfr for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent kind of liver cancer and a major health concern all over the globe. A potential molecular target for the treatment of HCC is the Epidermal Growth Factor Receptor (EGFR). This study used advanced computational tools and big data analysis to identify and evaluate potential herbal phytoconstituents that could target EGFR. We collected a library of phytoconstituents from various databases and retrieved their chemical structures from PubChem, Zinc-15, and ChEMBL. We also obtained the 3D structure of EGFR from the Protein Data Bank (PDB). To set up the protein structure and see the ligands and receptors, we utilized UCSF Chimera and BIOVIA Discovery Studio. To forecast the ligands' binding affinities to the protein, we employed molecular docking. We chose the ligands with the best conformation scores and Root-Mean-Square Deviation (RMSD) based on the docking findings. The ADME profile was further examined for the chosen ligands. We used the online servers admetSAR and Swiss ADME to evaluate the pharmacokinetic and pharmacodynamic properties of the top phytochemicals. Our study shows that advanced computational tools and techniques can be used to identify and evaluate potential drug candidates for HCC therapy. This approach can help to reduce the time and cost associated with drug discovery and design.