Receptor-specific scoring functions derived from quantum chemical models improve affinity estimates for in-silico drug discovery

The adaptation of forcefield-based scoring function to specific receptors remains an important challenge for in-silico drug discovery. Here we compare binding energies of forcefield-based scoring functions with models that are reparameterized on the basis of large-scale quantum calculations of the receptor. We compute binding energies of eleven ligands to the human estrogen receptor subtype α (ERα) and four ligands to the human retinoic acid receptor of isotype γ (RARγ). Using the FlexScreen all-atom receptor-ligand docking approach, we compare docking simulations parameterized by quantum-mechanical calculation of a large protein fragment with purely forcefield-based models. The use of receptor flexibility in the FlexScreen permits the treatment of all ligands in the same receptor model. We find a high correlation between the classical binding energy obtained in the docking simulation and quantum mechanical binding energies and a good correlation with experimental affinities R = 0.81 for ERα and R = 0.95 for RARγ using the quantum derived scoring functions. A significant part of this improvement is retained, when only the receptor is treated with quantum-based parameters, while the ligands are parameterized with a purely classical model.