Docking guidance with experimental ligand structural density improves docking pose prediction and virtual screening performance

Recent advances in structural biology have led to the publica- tion of a wealth of high resolution x-ray crystallography and cryo-EM macromolecule structures, including many complexes with small molecules of interest for drug design. While it is com- mon to incorporate information from the atomic coordinates of these complexes into docking (e.g. pharmacophore models or scaffold hopping), there are limited methods to directly leverage the underlying density information. This is desirable because it does not rely on the determination of relevant coordinates, which may require expert intervention, but instead interprets all density as indicative of regions to which a ligand may be bound. To do so, we have developed CryoXKit, a tool to incorporate ex- perimental densities from either cryo-EM or x-ray crystallogra- phy as a biasing potential on heavy atoms during docking. Using this structural density guidance with AutoDock-GPU, we found significant improvements in re-docking and cross-docking, im- portant pose prediction tasks, compared with the unmodified AutoDock4 force field. Failures in cross-docking tasks are addi- tionally reflective of changes in positioning of pharmacophores in the site, suggesting it is a fundamental limitation of trans- ferring information between complexes. We additionally found, against a set of targets selected from the LIT-PCBA dataset, that rescoring of these improved poses leads to better discriminatory power in virtual screenings for selected targets. Overall, Cry- oXKit provides a user-friendly method for improving docking performance with experimental data while requiring no a pri- ori pharmacophore definition and at virtually no computational expense.