rewirebio.iobenchmarks
Evidence claim

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.

6 reviewed evaluations

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

Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statementOriginal source and locationReview and provenance
Citation locations
1 values
  • 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
Context-only references
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.citation_locators

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

Comparison group
ml-vs-fep
Context-only references
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.comparison_group

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

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

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.comparison_title

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

Constraints
4 values
  • 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.
Context-only references
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.constraints

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

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

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.endpoint

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

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

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.field

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

Headline metric
kendall-tau
Context-only references
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.headline_metric

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

Limitations
6 values
  • 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.
Context-only references
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.limitations

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

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Context-only references
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction, supplementary appendix

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.pins

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

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

Original source ↗

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)
Retrieved: 2026-10-22T11:52:00Z

not individually reviewed

No individual claim review recorded

Audit details

Field: attributes.rationale

Source artifact SHA-256: be867a5d92add8b223d47d882a4213ad489741f3e4d5081a0901a54370342569

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

Inspected artifact

How to read the evidence

Sources and history

Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

1 source record and release historyDownload this release (gzip)
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
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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.
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