Comparing Neural-Network Scoring Functions and the State of the Art: Applications to Common Library Screening

We compare established docking programs, Auto Dock Vina and Schrodinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.