source · discovered

DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

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: deelig-2021

areas: molecular-interactions
url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8274096/
version: PMC archival version PMC8274096.1
retrieved at: 2026-09-15T23:29:32Z
doi: 10.1177/11779322211030364
publication status: peer_reviewed
year: 2021
artifact sha256: 5a7620c18d0622561004e1e25b5cfaf7399e93df3547eeefdd4cf6d300bb8aba
artifact url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC8274096/fullTextXML
artifact retrieved at: 2026-09-16T10:33:55.586Z
legacy paper: id: deelig-2021; title: DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity; year: 2021; publication status: peer_reviewed; version: PMC archival version PMC8274096.1; source url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8274096/; primary domain: molecular-interactions; retrieved utc: 2026-09-15T23:29:32Z; notes: Primary full text verified using Europe PMC XML; venue: Bioinformatics and Biology Insights; PMC ID: PMC8274096.; doi: 10.1177/11779322211030364
scope decision: included
missing metadata: licence: not_reported_in_legacy_extract

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