Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

The ever-increasing need for effective therapeutic management of thyroid cancer (TC) necessitates the exploration of novel approaches for advanced drug discovery. The current study employed a robust computational pipeline integrating Machine Learning (ML) algorithms, QSAR modeling, molecular docking, molecular dynamics (MD), density functional theory (DFT), and network pharmacology to identify novel Anaplastic Lymphoma Kinase (ALK) tyrosine kinase inhibitors. An initial library of 3546 compounds from the CHEMBL4247 database was systematically filtered to 578. This screening utilized Lipinski's rule of five, aided by QSAR and detailed PaDEL descriptor analysis. An ensemble ML model, specifically a Voting Classifier (VC) combining XGBoost, LightGBM, and ExtraTrees algorithms, attained high predictive accuracy (ROC-AUC = 0.99), facilitating a strong classification and prioritization of active leads. Molecular docking experiment identified five top hit ligands (60, 63, 124, 130, 204) having docking score ranging from -9.0 to -10.4 kcal/mol and also confirmed their strong binding affinities, which surpassed the native co-crystallized ligand used as a standard. Later on, ADMET studies were executed to explore their physicochemical properties. MD simulation trajectories and MM/PBSA analyses validated the notably conformational stability and favorable binding free energies of these hit complexes. Network pharmacology was incorporated to understand tentative mechanisms of action and potential off-targets, generating a protein-protein interaction (PPI) network. DFT-based frontier molecular orbital (FMO) analysis showed Ligand124 possessed the highest electrophilicity and optimal polarizability, consistent with its marked interaction stability in MD simulations. In addition, the molecular mechanisms of hit compounds against TC were elucidated using a network pharmacology approach, which revealed a compound-target network with crucial hub targets like AKT1 and TP53. Significant correlations with cancer-related pathways, such as PI3K-Akt and MAPK signaling, as well as key involvement in kinase activity, phosphorylation, and membrane signaling complexes, were observed by the enrichment analysis of the main targets. These comprehensive results imply that investigated hit compounds probably modulate the oncogenic signaling networks, especially those controlling cell survival, proliferation, and drug resistance, in order to achieve its anti-TC therapeutic actions. These findings highlight the fundamental ability of integrating ML and computational chemistry to accelerate therapeutic development for TC.