Evidence claim
Evidence summary: structural hypotheses for experiments
Summary of Choose structural hypotheses to guide experiments
Two new comparisons join the existing docking and FoldBench evidence, both on antibody complexes, and neither ties a prediction to an experiment's outcome. Fromm et al. 2026 generated 200 AlphaFold3 models for each of 110 antibody-antigen complexes deposited after the training cutoff, then asked which model each confidence score picks. Every score a user can compute picked models of about the same quality: mean DockQ 0.375 for ranking confidence, 0.370 for ipTM, 0.369 for pTM, 0.353 for ipSAE and 0.352 for pDockQ2, while the best of the 200 models averaged 0.544. The per-target correlation between score and accuracy stayed at or below 0.247, so within one target the confidence ranking carried little information. Smorodina et al. 2026, a bioRxiv preprint that has not been peer reviewed, ran AlphaFold3, Boltz-2, Boltz-1 and Chai-1 on 106 nanobody-antigen complexes. Sampling raised the best available model for every tool, with median best DockQ rising from 0.24 to 0.68 for AlphaFold3 and from 0.57 to 0.80 for Boltz-2 between 1 and 100 samples, though picking that best sample needs the experimental structure. Confidence tracked accuracy unevenly: the correlation between ipTM and best-sample DockQ was 0.736 for AlphaFold3, 0.665 for Boltz-2 and 0.612 for Chai-1, with 18% of Boltz-2 predictions confident but wrong and 24% of Chai-1 predictions accurate but unconfident. Pairing every nanobody with every antigen showed that confidence does not indicate that an interaction exists at all: average precision for ranking the real pairings above shuffled ones was 0.187 for AlphaFold3, 0.067 for Chai-1 and 0.026 for Boltz-2, against a baseline of about 0.011. The preprint's nanobody set mixes systems inside and outside each tool's training data, and its non-cognate pairings are assumed not to bind rather than tested. A high confidence score is a statement about a predicted structure, not evidence that two molecules interact.
Plain-language summary of what the reviewed evidence shows. Draft until reviewed.
Evidence
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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.
14 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Claimed field summary Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. 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 |
| Claimed field summary Context-only references | Structural Plausibility Without Binding Specificity: Limits of AI-Based Antibody-Antigen Structure Prediction Confidence Scores structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv version 1, posted 2026-03-03; Europe PMC preprint full text PPR1221387 (manuscript EMS215481); not peer reviewed | 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 |
Pinned records194 values
| Evaluating deep learning based structure prediction methods on antibody-antigen complexes structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. 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 |
Pinned records194 values
| Structural Plausibility Without Binding Specificity: Limits of AI-Based Antibody-Antigen Structure Prediction Confidence Scores structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv version 1, posted 2026-03-03; Europe PMC preprint full text PPR1221387 (manuscript EMS215481); not peer reviewed | 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 |
| Source location structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. 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 |
| Source location structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Context-only references | Structural Plausibility Without Binding Specificity: Limits of AI-Based Antibody-Antigen Structure Prediction Confidence Scores structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv version 1, posted 2026-03-03; Europe PMC preprint full text PPR1221387 (manuscript EMS215481); not peer reviewed | 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 |
| Claimed value Two new comparisons join the existing docking and FoldBench evidence, both on antibody complexes, and neither ties a prediction to an experiment's outcome. Fromm et al. 2026 generated 200 AlphaFold3 models for each of 110 antibody-antigen complexes deposited after the training cutoff, then asked which model each confidence score picks. Every score a user can compute picked models of about the same quality: mean DockQ 0.375 for ranking confidence, 0.370 for ipTM, 0.369 for pTM, 0.353 for ipSAE and 0.352 for pDockQ2, while the best of the 200 models averaged 0.544. The per-target correlation between score and accuracy stayed at or below 0.247, so within one target the confidence ranking carried little information. Smorodina et al. 2026, a bioRxiv preprint that has not been peer reviewed, ran AlphaFold3, Boltz-2, Boltz-1 and Chai-1 on 106 nanobody-antigen complexes. Sampling raised the best available model for every tool, with median best DockQ rising from 0.24 to 0.68 for AlphaFold3 and from 0.57 to 0.80 for Boltz-2 between 1 and 100 samples, though picking that best sample needs the experimental structure. Confidence tracked accuracy unevenly: the correlation between ipTM and best-sample DockQ was 0.736 for AlphaFold3, 0.665 for Boltz-2 and 0.612 for Chai-1, with 18% of Boltz-2 predictions confident but wrong and 24% of Chai-1 predictions accurate but unconfident. Pairing every nanobody with every antigen showed that confidence does not indicate that an interaction exists at all: average precision for ranking the real pairings above shuffled ones was 0.187 for AlphaFold3, 0.067 for Chai-1 and 0.026 for Boltz-2, against a baseline of about 0.011. The preprint's nanobody set mixes systems inside and outside each tool's training data, and its non-cognate pairings are assumed not to bind rather than tested. A high confidence score is a statement about a predicted structure, not evidence that two molecules interact. Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. 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 |
| Claimed value Two new comparisons join the existing docking and FoldBench evidence, both on antibody complexes, and neither ties a prediction to an experiment's outcome. Fromm et al. 2026 generated 200 AlphaFold3 models for each of 110 antibody-antigen complexes deposited after the training cutoff, then asked which model each confidence score picks. Every score a user can compute picked models of about the same quality: mean DockQ 0.375 for ranking confidence, 0.370 for ipTM, 0.369 for pTM, 0.353 for ipSAE and 0.352 for pDockQ2, while the best of the 200 models averaged 0.544. The per-target correlation between score and accuracy stayed at or below 0.247, so within one target the confidence ranking carried little information. Smorodina et al. 2026, a bioRxiv preprint that has not been peer reviewed, ran AlphaFold3, Boltz-2, Boltz-1 and Chai-1 on 106 nanobody-antigen complexes. Sampling raised the best available model for every tool, with median best DockQ rising from 0.24 to 0.68 for AlphaFold3 and from 0.57 to 0.80 for Boltz-2 between 1 and 100 samples, though picking that best sample needs the experimental structure. Confidence tracked accuracy unevenly: the correlation between ipTM and best-sample DockQ was 0.736 for AlphaFold3, 0.665 for Boltz-2 and 0.612 for Chai-1, with 18% of Boltz-2 predictions confident but wrong and 24% of Chai-1 predictions accurate but unconfident. Pairing every nanobody with every antigen showed that confidence does not indicate that an interaction exists at all: average precision for ranking the real pairings above shuffled ones was 0.187 for AlphaFold3, 0.067 for Chai-1 and 0.026 for Boltz-2, against a baseline of about 0.011. The preprint's nanobody set mixes systems inside and outside each tool's training data, and its non-cognate pairings are assumed not to bind rather than tested. A high confidence score is a statement about a predicted structure, not evidence that two molecules interact. Context-only references | Structural Plausibility Without Binding Specificity: Limits of AI-Based Antibody-Antigen Structure Prediction Confidence Scores structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv version 1, posted 2026-03-03; Europe PMC preprint full text PPR1221387 (manuscript EMS215481); not peer reviewed | 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 Plain-language summary of what the reviewed evidence shows. Draft until reviewed. Context-only references | Evaluating deep learning based structure prediction methods on antibody-antigen complexes structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. 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 Plain-language summary of what the reviewed evidence shows. Draft until reviewed. Context-only references | Structural Plausibility Without Binding Specificity: Limits of AI-Based Antibody-Antigen Structure Prediction Confidence Scores structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36 Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: bioRxiv version 1, posted 2026-03-03; Europe PMC preprint full text PPR1221387 (manuscript EMS215481); not peer reviewed | 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 2026-11-02-a7f03c9e55a5 · Record review: source checked
2 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
- Structural Plausibility Without Binding Specificity: Limits of AI-Based Antibody-Antigen Structure Prediction Confidence Scores · Original source · bioRxiv version 1, posted 2026-03-03; Europe PMC preprint full text PPR1221387 (manuscript EMS215481); not peer reviewed
Technical metadata and extraction receipts
Stable ID: use-case-summary-structural-hypotheses-experiments
- field
- summary
- value
- Two new comparisons join the existing docking and FoldBench evidence, both on antibody complexes, and neither ties a prediction to an experiment's outcome. Fromm et al. 2026 generated 200 AlphaFold3 models for each of 110 antibody-antigen complexes deposited after the training cutoff, then asked which model each confidence score picks. Every score a user can compute picked models of about the same quality: mean DockQ 0.375 for ranking confidence, 0.370 for ipTM, 0.369 for pTM, 0.353 for ipSAE and 0.352 for pDockQ2, while the best of the 200 models averaged 0.544. The per-target correlation between score and accuracy stayed at or below 0.247, so within one target the confidence ranking carried little information. Smorodina et al. 2026, a bioRxiv preprint that has not been peer reviewed, ran AlphaFold3, Boltz-2, Boltz-1 and Chai-1 on 106 nanobody-antigen complexes. Sampling raised the best available model for every tool, with median best DockQ rising from 0.24 to 0.68 for AlphaFold3 and from 0.57 to 0.80 for Boltz-2 between 1 and 100 samples, though picking that best sample needs the experimental structure. Confidence tracked accuracy unevenly: the correlation between ipTM and best-sample DockQ was 0.736 for AlphaFold3, 0.665 for Boltz-2 and 0.612 for Chai-1, with 18% of Boltz-2 predictions confident but wrong and 24% of Chai-1 predictions accurate but unconfident. Pairing every nanobody with every antigen showed that confidence does not indicate that an interaction exists at all: average precision for ranking the real pairings above shuffled ones was 0.187 for AlphaFold3, 0.067 for Chai-1 and 0.026 for Boltz-2, against a baseline of about 0.011. The preprint's nanobody set mixes systems inside and outside each tool's training data, and its non-cognate pairings are assumed not to bind rather than tested. A high confidence score is a statement about a predicted structure, not evidence that two molecules interact.
- source locator
- structural-20261009-source-fromm2026: Table 1, sections 2.1 and 2.2; structural-20261009-source-smorodina2026: Results P8, P11, P19 and P36
- review
- method: source-hash-verification; independent-cell-check; automated-source-review; reviewer: claude; reviewer note: Separate Claude review agent, independent of the collector who drafted the summary proposal; the reviewer corrected the text; no human review claimed; reviewed at: 2026-10-10T00:30:00Z; note: Drafted by the collector in coverage.json summary_proposal and corrected by the reviewer; every number traced to stored source_checked results or a count printed in a source. Descriptive summary only; no execution, human scientific review or clinical validation. See docs/reviews/use-cases/structural-hypotheses-experiments-2026-10-09.md.
- pins
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