A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases
Primary paper retained with its original identifier. Metadata inherited from the literature collection; individual result checks are separate.
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
28 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.artifact_retrieved_at 2026-09-16T10:41:16.557756+00:00 Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.artifact_sha256 c356a1c65a0033e5ae18a05d4afab5495856c5b6869328ff49e13547a4801a57 Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.artifact_url https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12801289/fullTextXML Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.doi 10.1021/acs.jcim.5c02481 Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.doi 10.1021/acs.jcim.5c02481 Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.id mpro-pose-affinity-2025 Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.notes Primary full text via Europe PMC XML; venue: Journal of Chemical Information and Modeling; PMC ID: PMC12801289. Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.primary_domain molecular-interactions Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.publication_status peer_reviewed Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.legacy_paper.retrieved_utc 2026-09-15T23:25:00Z Source metadata | A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases No field-specific location recorded Version: version of record | catalogued No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
View linked audit checks and correction history
Release 2026-09-29-06401fd5b220 · Record review: discovered
Technical metadata and extraction receipts
Stable ID: mpro-pose-affinity-2025
- areas
- molecular-interactions
- url
- https://pmc.ncbi.nlm.nih.gov/articles/PMC12801289/
- version
- version of record
- retrieved at
- 2026-09-15T23:25:00Z
- doi
- 10.1021/acs.jcim.5c02481
- publication status
- peer_reviewed
- year
- 2025
- artifact sha256
- c356a1c65a0033e5ae18a05d4afab5495856c5b6869328ff49e13547a4801a57
- artifact url
- https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12801289/fullTextXML
- artifact retrieved at
- 2026-09-16T10:41:16.557756+00:00
- legacy paper
- id: mpro-pose-affinity-2025; title: A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein–Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases; year: 2025; publication status: peer_reviewed; version: version of record; source url: https://pmc.ncbi.nlm.nih.gov/articles/PMC12801289/; primary domain: molecular-interactions; retrieved utc: 2026-09-15T23:25:00Z; notes: Primary full text via Europe PMC XML; venue: Journal of Chemical Information and Modeling; PMC ID: PMC12801289.; doi: 10.1021/acs.jcim.5c02481
- scope decision
- included
- missing metadata
- licence: not_reported_in_legacy_extract