WikiSysop: Created page with "== Citation == Hansel-Harris, A.T., Tillack, A.F., Santos-Martins, D., Holcomb, M. and Forli, S. 2025. Docking guidance with experimental ligand structural density improves docking pose prediction and virtual screening performance. Protein Science. 34, 3 (2025), e70082. == Abstract == Recent advances in structural biology have led to the publication of a wealth of high-resolution x-ray crystallography (XRC) and cryo-EM macromolecule structures, including many complexe..."

2026-02-12T08:25:03Z

Created page with "== Citation == Hansel-Harris, A.T., Tillack, A.F., Santos-Martins, D., Holcomb, M. and Forli, S. 2025. Docking guidance with experimental ligand structural density improves docking pose prediction and virtual screening performance. Protein Science. 34, 3 (2025), e70082. == Abstract == Recent advances in structural biology have led to the publication of a wealth of high-resolution x-ray crystallography (XRC) and cryo-EM macromolecule structures, including many complexe..."

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== Citation ==

Hansel-Harris, A.T., Tillack, A.F., Santos-Martins, D., Holcomb, M. and Forli, S. 2025. Docking guidance with experimental ligand structural density improves docking pose prediction and virtual screening performance. Protein Science. 34, 3 (2025), e70082.

== Abstract ==

Recent advances in structural biology have led to the publication of a wealth
of high-resolution x-ray crystallography (XRC) and cryo-EM macromolecule
structures, including many complexes with small molecules of interest for
drug design. While it is common 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 experimental densities from either cryo-EM or XRC 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, important pose prediction tasks, compared with the
unmodified AutoDock4 force field. Failures in cross-docking tasks are additionally
reflective of changes in the positioning of pharmacophores in the
site, suggesting it is a fundamental limitation of transferring 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,
CryoXKit provides a user-friendly method for improving docking performance
with experimental data while requiring no a priori pharmacophore
definition and at virtually no computational expense. Map-modification code
available at: https://github.com/forlilab/CryoXKit.

== Keywords ==

== Links ==

https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.70082

== Related software ==

== Related methods ==

== Comments ==

2025Hansel Ligand - Revision history