FoXSight: Facilitating Computational Hit Identification in Crystallographic Fragment Screening Experiments
- 1University of Hamburg
Abstract
X-ray crystallographic fragment screening reveals how small molecules bind to proteins, providing important starting points for drug design. Identifying these weak binding events remains difficult when electron density signals are noisy, partially occupied or obscured by solvent, and campaigns may comprise thousands of datasets. Here, we present FoXSight, a computational method for hit identification in crystallographic compound screening. FoXSight applies Difference-of-Gaussian (DoG) filtering to multi-reference subtraction maps to detect recurring, target-specific density features associated with bound ligands. Its output maps remain visually interpretable and support subsequent ligand modeling. We evaluated FoXSight on in-house and public datasets spanning seven protein targets, comparing its maps with PanDDA where suitable comparison data were available. FoXSight was successfully used as the primary hit identification tool in a challenging crystallographic fragment screening campaign for human Cathepsin L, where the output of established methods was impractical to inspect systematically.
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BibTeX
@misc{schulze2026foxsight,
title = {{FoXSight: Facilitating Computational Hit Identification in Crystallographic Fragment Screening Experiments}},
author = {T. Schulze and A. Meents and M. Rarey and S. Falke and S. Guenther},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.01.763443},
url = {https://www.biorxiv.org/content/10.64898/2026.11.01.763443v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - FoXSight: Facilitating Computational Hit Identification in Crystallographic Fragment Screening Experiments
AU - T. Schulze
AU - A. Meents
AU - M. Rarey
AU - S. Falke
AU - S. Guenther
PY - 2026
DA - 2026/11/01
PB - bioRxiv
DO - 10.64898/2026.11.01.763443
UR - https://www.biorxiv.org/content/10.64898/2026.11.01.763443v1
AB - X-ray crystallographic fragment screening reveals how small molecules bind to proteins, providing important starting points for drug design. Identifying these weak binding events remains difficult when electron density signals are noisy, partially occupied or obscured by solvent, and campaigns may comprise thousands of datasets. Here, we present FoXSight, a computational method for hit identification in crystallographic compound screening. FoXSight applies Difference-of-Gaussian (DoG) filtering to multi-reference subtraction maps to detect recurring, target-specific density features associated with bound ligands. Its output maps remain visually interpretable and support subsequent ligand modeling. We evaluated FoXSight on in-house and public datasets spanning seven protein targets, comparing its maps with PanDDA where suitable comparison data were available. FoXSight was successfully used as the primary hit identification tool in a challenging crystallographic fragment screening campaign for human Cathepsin L, where the output of established methods was impractical to inspect systematically.
ER -