DockCADD: A streamlined in silico pipeline for the identification of potent ribosomal S6 Kinase 2 (RSK2) inhibitors

• Developed a novel AutoDock-RSK2 pipeline for streamlined virtual screening targeting RSK2. • Identified six potent pyran derivatives (L1-L6) with high docking scores, showing strong binding affinities to RSK2. • Conducted comprehensive ADME-Tox profiling to select compounds with favorable drug-likeness and safety profiles. • Performed molecular dynamics simulations to reveal stable interactions of selected compounds with RSK2. • Utilized MM-GBSA calculations to determine binding free energies, highlighting compound L4 as the most promising RSK2 inhibitor. The search for innovative therapeutic strategies remains critical in addressing cancer, one of the leading global health challenges. Ribosomal S6 Kinase 2 (RSK2), a serine/threonine kinase, has emerged as a promising target for cancer therapy because it is implicated in oncogenic signaling. Herein, we developed an open-source computational pipeline, identified as DockCADD (available at https://github.com/mehdikariim/DockCADD ), which enables the identification of potent RSK2 inhibitors by automated virtual screening, ADME-Tox profiling, and molecular dynamics (MD) simulations. Employing pyran derivatives as the scaffold, top-scoring inhibitors as identified by the pipeline showed scores ranging from -9.46 to -9.89 kcal/mol and binding free energies ranging from -53.731 to -55.193 kcal/mol. Ligands L1, L2 and L3 showed stable binding within the ATP-binding pocket, wherein the compounds undergo slight structural distortions with a favorable van der Waal's interaction. The ligand L3 has exhibited the highest MM-GBSA binding free energy (-55.193 kcal/mol), which so far presents the most promising candidate. These results have pointed out the use of DockCADD as an efficient tool for the fast and low-cost process of drug discovery; L1–L3 should be further validated experimentally for cancer therapy.