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.