Carborane Clusters in Computational Drug Design: A Comparative Docking Evaluation Using AutoDock, FlexX, Glide, and Surflex

Compounds containing boron atoms play increasingly important roles in the therapy and diagnosis of various diseases, particularly cancer. However, computational drug design of boron-containing therapeutics and diagnostics is hampered by the fact that many software packages used for this purpose lack parameters for all or part of the various types of boron atoms. In the present paper, we describe simple and efficient strategies to overcome this problem, which are based on the replacement of boron atom types with carbon atom types. The developed methods were validated by docking closo- and nido-carboranyl antifolates into the active site of a human dihydrofolate reductase (hDHFR) using AutoDock, Glide, FlexX, and Surflex and comparing the obtained docking poses with the poses of their counterparts in the original hDHFR-carboranyl antifolate crystal structures. Under optimized conditions, AutoDock and Glide were equally good in docking of the closo-carboranyl antifolates followed by Surflex and FlexX, whereas Autodock, Glide, and Surflex proved to be comparably efficient in the docking of nido-carboranyl antifolates followed by FlexX. Differences in geometries and partial atom charges in the structures of the carboranyl antifolates resulting from different data sources and/or optimization methods did not impact the docking performances of AutoDock or Glide significantly. Binding energies predicted by all four programs were in accordance with experimental data.