Identification, experimental validation, and computational evaluation of potential ALK inhibitors through hierarchical virtual screening

Anaplastic Lymphoma Kinase (ALK) plays a pivotal oncogenic role in the onset and progression of malignancies such as non-small cell lung cancer, lymphoma, and neuroblastoma. ALK gene mutations or rearrangements significantly enhance tumour cell proliferation and survival. However, the emergence of resistance to existing ALK inhibitors in clinical settings remains a major challenge. Consequently, the development of next-generation inhibitors targeting ALK-resistant mutations has become a central focus in the field of anticancer drug discovery. In this study, a hierarchical virtual screening strategy based on protein structure was utilized to screen 87,454 ligand conformations from 50,000 compounds in the Topscience drug-like database. Structural clustering analysis and ADMET drug-likeness predictions led to the identification of two potential ALK inhibitors, F6524–1593 and F2815–0802. Subsequent activity validation, molecular docking, and molecular dynamics simulations elucidated their potential binding modes and mechanisms of action. This study provides valuable theoretical insights for the development of novel ALK inhibitors targeting drug-resistant mutations and offers guidance for optimizing ALK-targeted therapeutic strategies.