Discovering best candidates for Hepatocellular Carcinoma (HCC) by in-silico techniques and tools

Protein-ligand interaction plays an important role in structural-based drug designing. The aim of this work is to select new possible candidates for HCC by in-silico drug design using bioinformatics techniques and tool. Essential proteins were targeted for HCC metastasis; drugs were designed for them by using ligand-based drug design based on active model drugs. The study considered BCL-XL and FGF proteins and the commercially available drugs against HCC. The receptor was docked to those drugs and the energy values were obtained using the Molecular Operating Environment (MOE) docking software. According to the obtained energy values, we have chosen the best drugs. Also, the aim was to improve the binding efficiency and steric compatibility of the obtained drug by improving the Absorption Distribution Metabolism Excretion Toxicity (ADMET) properties of the analogues using available in-silico ADMET tools. The results of molecular docking identified 10 candidates for FGF and 17 candidates for BCL-XL. After the ADMET studies, these candidates are reduced to only 2 best candidates for FGF and 1 best candidate for BCL-XL.