Multiscale Computational and Pharmacophore-Based Screening of ALK Inhibitors with Experimental Validation

Background: Anaplastic lymphoma kinase (ALK) is a key receptor tyrosine kinase involved in regulating signaling pathways critical for cell proliferation, differentiation, and survival. Mutations or rearrangements of the ALK gene lead to aberrant kinase activation, driving tumorigenesis in various cancers. Although ALK inhibitors have shown clinical benefits, drug resistance remains a significant barrier to long-term efficacy. Developing novel ALK inhibitors capable of overcoming resistance is therefore essential. Methods: A structure-based pharmacophore model was constructed using the 3D structures of five approved ALK inhibitors. Systematic virtual screening of the Topscience drug-like database was performed incorporating PAINS filtering, ADMET prediction, and molecular docking to identify promising candidates. In vitro antiproliferative assays, molecular docking, molecular dynamics simulations, and MM/GBSA binding free energy calculations were used to evaluate biological activity and elucidate binding mechanisms. Results: Two candidates, F1739-0081 and F2571-0016, were identified. F1739-0081 exhibited moderate antiproliferative activity against the A549 cell line, suggesting potential for further optimization. Computational analyses revealed its probable binding modes and interactions with ALK, supporting the observed activity. Conclusions: This study successfully identified novel ALK inhibitor candidates with promising biological activity. The integrated computational and experimental approach provides valuable insights for the rational design of optimized ALK inhibitors to address drug resistance in cancer therapy.