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

Abstract

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.