WikiSysop: Created page with "== Citation == Karolczak, J., Przybyłowska, A., Szewczyk, K., Taisner, W., Heumann, J.M., Stowell, M.H., Nowicki, M. and Brzezinski, D. 2025. Ligand identification in CryoEM and X-ray maps using deep learning. Bioinformatics. 41, 1 (2025), btae749. == Abstract == Motivation: Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM),..."

2025-08-29T09:32:48Z

Created page with "== Citation == Karolczak, J., Przybyłowska, A., Szewczyk, K., Taisner, W., Heumann, J.M., Stowell, M.H., Nowicki, M. and Brzezinski, D. 2025. Ligand identification in CryoEM and X-ray maps using deep learning. Bioinformatics. 41, 1 (2025), btae749. == Abstract == Motivation: Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM),..."

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== Citation ==

Karolczak, J., Przybyłowska, A., Szewczyk, K., Taisner, W., Heumann, J.M., Stowell, M.H., Nowicki, M. and Brzezinski, D. 2025. Ligand identification in CryoEM and X-ray maps using deep learning. Bioinformatics. 41, 1 (2025), btae749.

== Abstract ==

Motivation: Accurately identifying ligands plays a crucial role in the process of structure-guided drug
design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy
(cryoEM), scientists verify whether small-molecule ligands bind to active sites of interest. However, the
interpretation of density maps is challenging, and cognitive bias can sometimes mislead investigators
into modeling fictitious compounds. Ligand identification can be aided by automatic methods, but
existing approaches are available only for X-ray diffraction and are based on iterative fitting or featureengineered
machine learning rather than end-to-end deep learning.
Results: Here, we propose to identify ligands using a deep learning approach that treats density maps
as 3D point clouds. We show that the proposed model is on par with existing machine learning methods
for X-ray crystallography while also being applicable to cryoEM density maps. Our study demonstrates
that electron density map fragments can be used to train models that can be applied to cryoEM
structures, but also highlights challenges associated with the standardization of electron microscopy
maps and the quality assessment of cryoEM ligands.
Availability: Code and model weights are available on GitHub at https://github.com/jkarolczak/ligandsclassification.
Datasets used for training and testing are hosted at Zenodo: 10.5281/zenodo.10908325.

== Keywords ==

== Links ==

https://academic.oup.com/bioinformatics/article/41/1/btae749/7928841

== Related software ==

== Related methods ==

== Comments ==

2025Karolczak Ligand - Revision history