There have been a few attempts to use computational techniques to find p38 kinase inhibitors, using structure-based virtual screening or structure-based design of novel inhibitors. However, there has been no use of deep learning (DL) for the repurposing of FDA-approved drugs as p38 inhibitors that we are aware of. Deep learning is a form of machine learning that mimics the human brain and its networks of neurons. It has applications in a variety of fields like healthcare, cybersecurity and even video games, but it has recently Vermani 4 emerged as one of the most effective machine-learning techniques in all aspects of drug discovery, from target prediction to synthesis to identification of prognostic biomarkers. Furthermore, data-filtration techniques like fingerprint clustering and feature selection have both been shown to significantly increase deep-learning accuracy. That's why in this study we utilized these data preparation algorithms along with a deep neural network to discover which FDA approved drugs could also function as p38 inhibitors, revealing 149 candidates. These predictions were then ranked through ligand-docking, a structure-based virtual screening method that shows sufficient accuracy in determining how thermodynamically favorable the binding of a ligand to a protein is. With corroboration from experimental trials, the most promising of these drugs could be used as treatment for BAG3 P209L MFM or other diseases with similar p38-mediated pathological mechanisms.