肝细胞癌
虚拟筛选
计算机科学
医学
数据科学
药物发现
生物信息学
癌症研究
生物
作者
Rushan Fei,Na Lin,Xin Zhang,Lei Xu,Qingnan Zhang,Zhichao Pan,Xiaowu Dong,Weilin Wang
标识
DOI:10.1021/acsmedchemlett.5c00173
摘要
Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, with the efficacy of current targeted therapies limited by drug resistance and adverse effects. The receptor tyrosine kinase c-MET has been identified as a promising target for HCC therapy due to its involvement in tumor progression, metastasis, and poor prognosis. However, no c-MET inhibitors have been approved for HCC treatment. This study integrates a multistep virtual screening workflow, incorporating molecular docking, machine learning-based predictions, and molecular dynamics simulations, to identify novel c-MET inhibitors with unique structural frameworks. Among several promising candidates, compound 10 exhibited potent c-MET inhibition and selective antiproliferative effects against the HCC cell line Hep3B. Further molecular dynamics simulations confirmed the binding stability of compound 10 with c-MET, highlighting key interactions that contribute to its inhibitory activity. These findings provide valuable insights into the development of c-MET inhibitors with potential therapeutic applications for HCC.
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