Our conformational search resulted in 8890 conformers of p38 inhibitors predicted by our neural network, and 6616 conformers of already known p38 inhibitors along with 5473 conformations of random FDA drugs added as negative fits. These conformers were then docked with p38 and given Vermani 10 a final GBVI/WSA score. The random control compounds had an average docking score of -5.92 kcal/mol, the known inhibitors had a score of -7.30 and the predicted inhibitors had a score of -7.45 (Figure ). A t-Test (Table ) confirmed that the predicted inhibitors were statistically significantly better at binding to p38 at the ATP-binding site-therefore inhibiting p38's function-than random FDA-approved drugs (Since the docking results measure binding energy, a more negative value means stronger binding). The t-Test also shows that the performance of the predicted inhibitors is much more consistent, with a variance less than half that of the random control group. The compounds exhibiting the best docking energies are listed in Table and displayed with the p38 binding pocket in Figure . Table 2 also includes the original uses and protein interactions of the top 40 predicted inhibitors according to docking score, as well as their clusters (see section 3.2). Of these 40, 34 already act as some kind of protein inhibitor and 14 (35%) act on protein kinases specifically, which share structural similarities with p38. By comparison, 52 of the 2151 FDA-approved drugs (only 2.4%) used in this project are protein kinases. This means that the model and docking simulations are successfully identifying drugs that are already clinically proven to be inhibitors of proteins structurally similar to p38. Table shows the 10 residues, which interacted most frequently with the predicted compounds.