Recent advances in the metal-organic framework (MOF) have accelerated the discovery of novel metal-based anticancer, antibacterial and antimalarial compounds. This is substantiated by many serendipitously discovered metals (Ru, Rh, and Ir) based inhibitors that established the importance of metal inserted into the known organic scaffold. Conversely, it is possible to design novel bioactive compounds by mimicking hypervalent carbon atoms by transition metals. This process can be facilitated by computational drug discovery by treating metal center using optimized parameters that can be used for molecular docking and molecular dynamics simulations. Further, the method can be plugged with high computational power and refined algorithms to interpret chemical phenomena with atomic-level insights. In the present work, we have demonstrated an approach for parameterizing three organometallic ligands (FLL, E52, and staurosporine) using MCPB.py. In particular, we report that E52 and FLL have a better shape complimentary and affinity compared to staurosporine identified inhibitor (staurosporine) against Calcium-dependent protein kinases 2 (CDPK2). This study also revealed that a flexible approach (ensemble) outperforms for the given target with dynamic movements. The calculated MMPBSA energies for staurosporine, FLL and E52 were −66.461 ± 2.192, −67.182 ± 1.971 and −91.339 ± 2.745 kcal/mol respectively.