Multi-scale computational analysis of monoacylglycerol lipase inhibition as a new therapeutic strategy for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) remains one of the most critical global health challenges, particularly in connection to metabolically linked diseases like metabolic dysfunction-associated steatotic liver disease (MASLD). Current studies established a relationship between monoacylglycerol lipase (MAGL), a critical enzyme in lipid signal transduction pathways, and HCC development through control of pro-tumorigenic lipid signal modulators. The research presented here utilized an integrative computational pipeline to identify new sources of MAGL inhibitors with potential therapeutic value and the preliminary high-throughput screening yielded three lead candidates. Among these lead candidates, compound 17475070 exhibited optimal digital features in DFT-based quantum mechanics assessments, predicting high stability and low reactivity. Re-docking experiments were in support of strong binding interactions, which in turn were further supported through an array of critical hydrogen bonds as well as hydrophobic contacts in the MAGL binding site. Extensive molecular dynamics simulations (500 ns) confirmed that 17475070 remained stably bound, with minimal con-formational drift and persistent active site engagement. Principal component and free energy landscape analysis demonstrated that the compound almost entirely populated one low-energy conformational basin indicative of strong and stable interaction. Additional confirmation in terms of QM/MM simulations established that it continues to maintain strong electronic interactions in the catalytic pocket of MAGL. Machine learning based ADMET profiling predicted that the compound 17475070 would exhibit good oral bioavailability, low toxicity risks, and a long biological half-life. These findings, in aggregate, designate 17475070 as an interesting candidate to further develop as a potential MAGL-targeted HCC therapy.