Toward an optimal docking and free energy calculation scheme in ligand design with application to COX-1 inhibitors.

Cyclooxygenase-1 (COX-1) is one of the main targets of most pain-relieving pharmaceuticals. Although the enzyme is well characterized, it is known to be a difficult target for automated molecular docking and scoring. We collected from the literature a structurally diverse set of 45 nonsteroidal anti-inflammatory drugs (NSAIDs) and COX-2-selective inhibitors (coxibs) with a wide range of binding affinities for COX-1. The binding of this data set to a homology model of human COX-1 was analyzed with different combinations of molecular docking algorithms, scoring functions, and the linear interaction energy (LIE) method for estimating binding affinities. It is found that the computational protocols for estimation of binding affinities are extremely sensitive to the initial orientations of the ligands in the binding pocket. To overcome this limitation, we propose a systematic exploration of docking poses using the LIE calculations as a postscoring function. This scheme yields predictions in excellent agreement with experiment, with a mean unsigned error of 0.9 kcal/mol for binding free energies and structures of high quality. A significant improvement of the results is also seen when averaging over experimental data from several independent measurements.