MAPKAPK2, a substrate of p38 MAPKs, plays central role in p38-mediated signal transduction, and its inhibitors are promisingly useful in the treatment of inflammatory diseases. The computational approaches comprising both ligand-based drug design and structure-based drug design were used as virtual screening strategies for the discovery of novel MK2 inhibitors. Two quantitative pharmacophore models were generated with a training set of 27 MK2 inhibitors using HypoGen module of CATALYST. The two models suggested that two hydrogen bond acceptors, one hydrogen bond donor, and one hydrophobic feature are essential for ligand binding. Further, 3D QSAR model with comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) was constructed using a training set of 42 inhibitors. The predictive CoMFA and CoMSIA models have cross-validated coefficients (q2) of 0.804 and 0.765 and regression coefficients (r2) of 0.984 and 0.986, respectively. The structure-based studies were carried out by docking 77 known MAPKAPK2 inhibitors into the active site of receptor using Glide and analyzing the "hotspots" of the active site. Docking studies revealed that Met138, Leu141, Asp207, Lys93, Cys140, Leu70, Thr206, and Gly143 are showing interactions with highly active compounds. The structure activity relationships elucidated here for carboline derivatives combined with their binding information will provide an integrated approach to explore the chemical space further for improving the potency of MAPKAPK2 inhibitors.