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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.

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Release 2026-09-16-d74d282221a9 · Record review: discovered

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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

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