
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.08.27.610022
preprint
1
Article
Ligand Identification using Deep Learning
Karolczak Jacek http://orcid.org/0000-0001-5414-960X

Przybyłowska Anna http://orcid.org/0009-0007-1773-996X

Szewczyk Konrad http://orcid.org/0000-0002-2432-7978

Taisner Witold http://orcid.org/0009-0001-4752-9476

Heumann John M. http://orcid.org/0000-0001-6751-3028

Stowell Michael H.B. http://orcid.org/0000-0001-7250-1419

Nowicki Michał http://orcid.org/0000-0002-2299-9025

Brzezinski Dariusz http://orcid.org/0000-0001-9723-525X

28 8 2024
2024.08.27.610022https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
http://biorxiv.org/lookup/doi/10.1101/2024.08.27.610022
nihpp-2024.08.27.610022.pdf
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 feature-engineered 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/ligands-classification . Datasets used for training and testing are hosted at Zenodo: 10.5281/zenodo.10908325 .

Contact

dariusz.brzezinski@cs.put.poznan.pl
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