Solvent-Site Prediction for Fragment Docking and Its Implication on Fragment-Based Drug Discovery

The accuracy in the posing and scoring of low-affinity fragments is still a main challenge in fragment-based virtual screenings. The positive impact of including structural or predicted water molecules during docking on the docking performance is discussed frequently and is not conclusive so far. We present a comprehensive statistical evaluation of the effect of including crystallographic or predicted water molecules on the docking performance of fragment redocking. Further, cross-docking fragments into binding sites occupied by larger ligands and vice versa were elucidated. These cross-dockings imitate realistic use cases of fragment hit identification and fragment growing or synthon-based virtual screenings, respectively. Therefore, a new benchmark data set, called Frag2Lead containing 103 fragment-protein and corresponding lead-protein complexes, was compiled. Inclusion of water molecules during docking had a general positive impact on docking performance, but the preferred combination of the docking tool and water model varied across the different targets. A consensus approach over multiple solvent models and docking tools turned out to be beneficial for both re- and cross-dockings. Implementing constraints by template docking or pharmacophore features is advantageous for pose prediction for fragment growing approaches.