Relevance of FEP+ benchmark, 876-complex OpenFE subset to ranking analogues of a hit
- Use case
- Rank close analogues of a hit by binding affinity
- Assessed by
- FEP+ benchmark OpenFE subset, affinity ranking
- Relevance
- Direct evidence
- Endpoint
- Target-average Pearson R, Kendall tau and absolute error of predicted against measured affinities over 876 protein-ligand complexes
Judgement of how closely this protocol answers the lead-optimisation ranking decision. Drafted by the extracting agent; not reviewed.
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.
19 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
Citation locations1 values
| Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
| Comparison group ml-vs-fep Context-only references | Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
| Comparison title Machine learning against free energy calculation on the same series Context-only references | Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
Constraints4 values
| Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
| Endpoint Target-average Pearson R, Kendall tau and absolute error of predicted against measured affinities over 876 protein-ligand complexes Context-only references | Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
| Claimed field links:assessed_by:uc-leadopt-20261022-protocol-boltz2-fep-openfe-subset Context-only references | Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
| Headline metric kendall-tau Context-only references | Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
Limitations6 values
| Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
Pinned records63 values
| Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
| Rationale This protocol puts a machine-learning affinity predictor beside two free energy workflows on the same hit-to-lead and lead-optimisation complexes with measured affinities, which is the comparison the use case exists to make, and it measures the use case's own endpoint: appendix D.2.2 computes each metric inside one assay and then averages across assays weighted by compound count, so a printed Pearson R is a compound-weighted mean of within-assay values rather than a correlation pooled over unrelated targets. The supplement does not state how the subsets were split into assays, nor that one assay is one congeneric series. It does not have to for this classification: appendix D.2.1 defines both subsets as held out from the FEP+ benchmark, and Ross et al. 2023 describes that collection as congeneric series with one FEP graph per series, so every compound scored here belongs to some congeneric series and an assay is at worst a subset of one. What would defeat the classification is an assay spanning several unrelated targets, which neither source suggests and which an assay, being tied to one protein target, does not admit. What stays unestablished is narrower: an assay could group more than one congeneric series of the same target, so a printed value is a mean over assay-level rather than strictly series-level correlations. The granularity is series-average: the values support 'on average over these congeneric series, this method ranked analogues this well', and do not support any statement about a specific series or about variance across series, because no per-series value and no across-series spread is printed. Context-only references | Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14 Version: bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344) | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: The recorded hash is of the supplement PDF itself, extracted from the Europe PMC supplementaryFiles ZIP (ZIP SHA-256 25050dabd9c66fdb955704095023037f1bbabc3a93b7b61f00344c75df8bdb70 on 2026-10-22; the ZIP is rebuilt per request and its hash is not stable). Archive member: NIHPP2025.06.14.659707v1-supplement-1.pdf |
Sources and history
Release 2026-10-22-1b7cf027e4f6 · Record review: source checked
1 source record and release history
- Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix · Original source · bioRxiv v1 posted 2025-06-18; supplement PDF as deposited in Europe PMC PMC12262699 (PPR1038344)
Technical metadata and extraction receipts
Stable ID: uc-leadopt-20261022-judgement-boltz2-fep-openfe-subset
- field
- links:assessed_by:uc-leadopt-20261022-protocol-boltz2-fep-openfe-subset
- value
- uc-leadopt-20261022-protocol-boltz2-fep-openfe-subset
- relevance
- direct
- endpoint
- Target-average Pearson R, Kendall tau and absolute error of predicted against measured affinities over 876 protein-ligand complexes
- rationale
- This protocol puts a machine-learning affinity predictor beside two free energy workflows on the same hit-to-lead and lead-optimisation complexes with measured affinities, which is the comparison the use case exists to make, and it measures the use case's own endpoint: appendix D.2.2 computes each metric inside one assay and then averages across assays weighted by compound count, so a printed Pearson R is a compound-weighted mean of within-assay values rather than a correlation pooled over unrelated targets. The supplement does not state how the subsets were split into assays, nor that one assay is one congeneric series. It does not have to for this classification: appendix D.2.1 defines both subsets as held out from the FEP+ benchmark, and Ross et al. 2023 describes that collection as congeneric series with one FEP graph per series, so every compound scored here belongs to some congeneric series and an assay is at worst a subset of one. What would defeat the classification is an assay spanning several unrelated targets, which neither source suggests and which an assay, being tied to one protein target, does not admit. What stays unestablished is narrower: an assay could group more than one congeneric series of the same target, so a printed value is a mean over assay-level rather than strictly series-level correlations. The granularity is series-average: the values support 'on average over these congeneric series, this method ranked analogues this well', and do not support any statement about a specific series or about variance across series, because no per-series value and no across-series spread is printed.
- constraints
- Keep the author-reported Boltz-2, GAT and BACPI rows distinct from the OpenFE and FEP+ rows, whose origin the supplement does not state.; Do not treat the results on this subset and on the four-target subset as independent; both derive from the same collection and must partly overlap.; Compare only cells that share the centering convention.; Do not combine these series-average values with per-series FEP+ or OpenFE values recovered from the baselines' own data releases: that would mix two granularities in one comparison, and no per-series value exists for Boltz-2.
- limitations
- Reported by the developers of the machine-learning model being promoted.; No per-series value and no uncertainty are printed; bootstrap error bars exist only in a figure with no printed numbers.; The FEP+ arm's protocol was tuned per system after seeing the error against experiment, which raises the baseline being compared against.; The column headers read 'target avg.' while the methods define the aggregation as a compound-weighted mean across assays; the source does not reconcile the two labels.; The OpenFE arm's cited source is a blog post, which is not a primary source under this repository's rules and was not retrieved.; No compute cost is printed in this table.
- comparison group
- ml-vs-fep
- comparison title
- Machine learning against free energy calculation on the same series
- stratum label
- FEP+ benchmark, 876-complex OpenFE subset
- stratum order
- 1
- headline metric
- kendall-tau
- citation locators
- source id: uc-leadopt-20261022-source-boltz2-supplement; locator: Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14
- source locator
- Appendix D.2.1 'Data leakage control'; D.2.2 'Evaluation metrics' and 'Baselines'; Tables 11, 12 and 14
- review
- method: source-hash-verification; pdf-text-parse; independent-cell-check; rendered-source-visual-check; ai-assisted-source-review; method note: Re-retrieved all three sources and matched each pinned SHA-256. Re-rendered the Boltz-2 supplement with pdftotext -layout (poppler 26.08.0) and matched the text layer's recorded SHA-256. Re-read all 256 cells with a parser written for this review that does not import the extraction script, matching each result to its cell through its own source_locator, and separately read appendix tables 11, 12 and 14 from PNG renderings of PDF pages 29 and 30 at 160 dpi, and Ross Table 3 from the pinned XML markup. Checked printed value, numeric value, locator, metric concept, qualifier, direction, unit, uncertainty and the linked configuration, protocol and dataset, and that every N/A or '-' cell is recorded as missing with a reason.; reviewer: claude; reviewer note: Claude review agent, independent of the extraction agent; no human review claimed; reviewed at: 2026-10-22T13:40:00Z; artifact sha256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12262699/supplementaryFiles; note: Independent review 2026-10-22: the quoted wording was matched against the pinned artifact and the locator checked.
- reviewed evaluations
- uc-leadopt-20261022-eval-t11-bacpi; uc-leadopt-20261022-eval-t11-boltz-2; uc-leadopt-20261022-eval-t11-boltz-2-iptm; uc-leadopt-20261022-eval-t11-fep-plus; uc-leadopt-20261022-eval-t11-gat; uc-leadopt-20261022-eval-t11-openfe
- pins
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- reason
- First evidence for this use case: a machine-learning affinity predictor beside two free energy workflows on the same 876 complexes, independently reviewed against its pinned artifact on 2026-10-22.