Ligand Identification in CryoEM and X-ray Maps Using Deep Learning.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39257821", "kind": "science-record", "revision": 2, "content_hash": "sha256:33ad4704630606b178bfcb2ea97e2a58f606234e18814d0508225f2671c86725", "created_at": "2026-11-01T20:29:24Z", "updated_at": "2026-11-01T21:33:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Ligand Identification in CryoEM and X-ray Maps Using Deep Learning.", "doi": "10.1101/2024.08.27.610022", "pmid": "39257821", "authors": ["Jacek Karolczak", "Anna Przybyłowska", "Konrad Szewczyk", "Witold Taisner", "John M Heumann", "Michael H B Stowell", "Michał Nowicki", "Dariusz Brzezinski"], "journal": "bioRxiv : the preprint server for biology", "year": 2024, "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.\nResults: 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 aid the training of models that can later be applied to cryoEM structures but also highlights challenges associated with the standardization of electron microscopy maps and the quality assessment of cryoEM ligands.\nAvailability: 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. An accompanying ChimeraX bundle is available at https://github.com/wtaisner/chimerax-ligand-recognizer.", "claims": [{"id": "claim-method", "statement": "For cryoEM evaluation, the 34,671 cryoEM ligands were divided into three folds and the models were evaluated using stratified 3-fold cross-validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39257821", "locator": "sections[12] \"Machine learning experimental setup\" paragraph 2", "quote": "For cryoEM evaluation, the 34,671 cryoEM ligands were divided into three folds and the models were evaluated using stratified 3-fold cross-validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Datasets used for training and testing are hosted at Zenodo: 10.5281/zenodo.10908325.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39257821", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Datasets used for training and testing are hosted at Zenodo: 10.5281/zenodo.10908325."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The proposed deep-learning model achieved performance comparable to existing machine-learning methods for X-ray crystallography while also being applicable to cryoEM density maps.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39257821", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "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."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The proposed deep-learning model achieved performance comparable to existing machine-learning methods for X-ray crystallography while also being applicable to cryoEM density maps.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39257821"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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