headline_finding: structural-20261009-protocol-fromm2026-abag-model-selection
Descriptive fact transcribed from the pinned source.
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.
6 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.field headline_finding Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes Section 3.3 paragraph 1; section 3.6 paragraph 4 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py |
| attributes.source_locator Section 3.3 paragraph 1; section 3.6 paragraph 4 Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes Section 3.3 paragraph 1; section 3.6 paragraph 4 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py |
| attributes.value For AlphaFold3 the mean DockQ of the top-ranked model rises only from 0.29 to 0.37 as sampling grows, while the best generated model reaches 0.52. For Boltz-1 and Chai-1 the top-ranked model barely improves with more samples (from 0.12 to 0.14). Scores computed from predicted aligned errors give a mean DockQ of about 0.35 for the selected model against 0.54 for the best model; the same scores computed from the true aligned errors close most of that gap. Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes Section 3.3 paragraph 1; section 3.6 paragraph 4 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py |
| description Descriptive fact transcribed from the pinned source. Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes Section 3.3 paragraph 1; section 3.6 paragraph 4 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py |
| Relationship: subject structural-20261009-protocol-fromm2026-abag-model-selection Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes Section 3.3 paragraph 1; section 3.6 paragraph 4 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py |
| name headline_finding: structural-20261009-protocol-fromm2026-abag-model-selection Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes Section 3.3 paragraph 1; section 3.6 paragraph 4 Version: Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: JATS XML parse (xml.etree), by extract/extract_structural.py |
Sources and history
Release 2026-10-10-6e93f504adfc · Record review: source checked
1 source records and release history
- Evaluating deep learning based structure prediction methods on antibody-antigen complexes · Original source · Bioinformatics 42(4):btag136, 2026; PMC13061134 full-text XML
Technical metadata and extraction receipts
Stable ID: structural-20261009-claim-fromm2026-top-vs-best
- field
- headline_finding
- value
- For AlphaFold3 the mean DockQ of the top-ranked model rises only from 0.29 to 0.37 as sampling grows, while the best generated model reaches 0.52. For Boltz-1 and Chai-1 the top-ranked model barely improves with more samples (from 0.12 to 0.14). Scores computed from predicted aligned errors give a mean DockQ of about 0.35 for the selected model against 0.54 for the best model; the same scores computed from the true aligned errors close most of that gap.
- source locator
- Section 3.3 paragraph 1; section 3.6 paragraph 4
- review
- method: transcription; reviewer: claude; date: 2026-10-09; reviewer note: Claude (Opus 5.5) extraction agent; extraction record, not an independent review; artifact sha256: a7320667ed6f7d8df440d90275d976b91d2fe98411172a79c6a304eb3f02275d; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13061134/fullTextXML; note: Hand transcription from the source text. Pending independent review.