Machine learning driven identification of therapeutic phytochemicals targeting Hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC), being the most common liver cancer, remains a global health concern due to the high mortality rate. HCC is also attributed to severe alcohol abuse, further leading to liver cirrhosis and cytochrome expression. The known treatments for HCC are becoming less effective with high side effects, which highlights the need for promising phytochemicals, as antioxidants, anti-inflammatories, antitumor, and other pharmacological properties. This study comprises a majorly utilized in vitro model for HCC, i.e., Huh 7 cell line, which was considered for retrieving the IC50 values of experimentally known inhibitors using the ChemBL database. Followed by many subsequent steps, Extra Trees Classifier and Light Gradient Boosting Machine (LGBM) showed the best performance of Receiver Operative Characteristic (ROC) of 0.91 and 0.90, respectively, as robust ML-based QSAR models. Furthermore, screening of the unknown phytochemicals and ADMET analysis showed optimum results for the phytochemicals: Bilobol, Corlumine, and Oliveotilic acid. Additionally, HSP90AA1 and CTNNB1, being the major targets with corlumine, had the best docking score of −8.66 kcal/mol and −5.21 kcal/mol, respectively, than the reference compound −8.31 kcal/mol for HSP90AA1 and −4.83 for CTNNB1 kcal/mol respectively. Further studies of molecular dynamic simulation, such as RMSD, RMSF, RG, SASA, and H-bond formation for CTNNB1- corlumine complex showed comparatively better results than HSP90AA1- corlumine complex. In a nutshell, corlumine phytochemicals, as an outcome from this study, may be used for in vitro and in vivo model testing as a novel compound as a pharmaceutical drug molecule for HCC inhibition.