A multistage computational pipeline integrating ligand-based and structure-based screening with ADMET evaluation, molecular dynamic simulation and free energy calculations for the discovery of potential TNIK inhibitors.
Dysregulation of metabolic signalling pathways, such as Wnt/β-catenin, is a hallmark of various cancers including colorectal cancer, lung squamous cell carcinoma, papillary thyroid carcinoma, multiple myeloma etc. TRAF2 and NCK-interacting kinase (TNIK), a serine/threonine kinase, plays a critical role in this pathway by phosphorylating TCF4, thereby promoting transcription of oncogenic genes linked to cell proliferation, stemness, and therapeutic resistance. Owing to its central role across multiple tumor types, TNIK is a promising target for small-molecule drug development. This study utilized an integrated computer-aided drug design (CADD) strategy to identify novel TNIK inhibitors. Ligand-based virtual screening (LBVS) was initiated using a pharmacophore model based on known inhibitors to screen over 1 billion PubChem compounds. Filtering via cheminformatics protocols (PAINS, Lipinski's rules, and molecular descriptors) in KNIME narrowed the library to 25,082 candidates. These were subjected to structure-based virtual screening (SBVS) using molecular docking against TNIK (PDB ID: 6RA7). The top 100 hits were assessed for ADMET properties using DruMAP v2.0, identifying three lead compounds: PubChem-11590224, -9168610, and -121049323. These were further validated through 200-ns molecular dynamics (MD) simulations in triplicate using GROMACS, confirming stable binding interactions. MM/PBSA free energy calculations revealed favorable binding energies, comparable to clinical-phase TNIK inhibitor INS018_055. This LBVS-SBVS-ADMET-MD pipeline effectively identified three promising TNIK inhibitors, providing a solid foundation for future experimental validation and potential development of targeted therapies for Wnt-driven malignancies.