Evidence claim

Relevance of FEP+ benchmark, four-target subset to ranking analogues of a hit

Use case: Rank close analogues of a hit by binding affinity
Assessed by: FEP+ benchmark four-target subset, affinity ranking and cost
Relevance: Direct evidence
Endpoint: Target-average Pearson R, Kendall tau and absolute error of predicted against measured affinities for 87 neutral compounds on four kinases, beside each method's compute cost

Judgement of how closely this protocol answers the lead-optimisation ranking decision. Drafted by the extracting agent; not reviewed.

10 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 statement	Original source and location	Review 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 5 values Read the time column as printed and do not derive a per-compound cost from it; the table does not say what it counts or on what hardware. Do not quote a measured FEP+ cost from this table; its FEP+ time cell is '-'. Four of the ten arms print N/A for every column beyond Pearson R and Kendall tau, so do not build a cross-method comparison on the error or within-threshold columns. Do not treat the results on this subset and on the 876-complex subset as independent. Do not combine these series-average values with per-series FEP+, OpenFE or ABFE 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 for 87 neutral compounds on four kinases, beside each method's compute cost 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-4target 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. The FEP+ arm's protocol was tuned per system after seeing the error against experiment, and the supplement says the ABFE arm's protocol was optimised on this same data set, so both physics baselines are optimistic. The time column has no stated denominator or hardware, and three of its cells are ranges or bounds rather than values. The series definition behind the 87 compounds rests on a citation that does not resolve in the supplement's reference list. Four targets is a small sample on which to average. 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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450e11f48399a8957a339116315c15c1c52841d75988f6aa0cea6eebc5053906 record id uc-leadopt-20261022-source-boltz2-supplement fields status attributes.artifact_sha256 sha256 6e31fabd2a46f1b867adbf58be440609b0060aca9e10abcfe9723259cef3bb46 record id use-case-lead-optimisation-affinity-ranking fields status name description facets attributes.question attributes.decision attributes.inputs attributes.output attributes.exclusions attributes.setting attributes.clinical_scope attributes.intended_users sha256 7432deaf6a9ac54d76ccc53abb33ca8b040bf88886f79afd081a08dd686511df 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 is the only extracted protocol that prints accuracy and compute cost side by side across a machine-learning predictor, two relative free energy workflows, an absolute free energy protocol, an end-state method and a docking score, which is both halves of the decision, 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. Four series is a small number to average over. 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

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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)

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Stable ID: uc-leadopt-20261022-judgement-boltz2-fep-4target

field: links:assessed_by:uc-leadopt-20261022-protocol-boltz2-fep-4target
value: uc-leadopt-20261022-protocol-boltz2-fep-4target
relevance: direct
endpoint: Target-average Pearson R, Kendall tau and absolute error of predicted against measured affinities for 87 neutral compounds on four kinases, beside each method's compute cost
rationale: This is the only extracted protocol that prints accuracy and compute cost side by side across a machine-learning predictor, two relative free energy workflows, an absolute free energy protocol, an end-state method and a docking score, which is both halves of the decision, 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. Four series is a small number to average over.
constraints: Read the time column as printed and do not derive a per-compound cost from it; the table does not say what it counts or on what hardware.; Do not quote a measured FEP+ cost from this table; its FEP+ time cell is '-'.; Four of the ten arms print N/A for every column beyond Pearson R and Kendall tau, so do not build a cross-method comparison on the error or within-threshold columns.; Do not treat the results on this subset and on the 876-complex subset as independent.; Do not combine these series-average values with per-series FEP+, OpenFE or ABFE 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.; The FEP+ arm's protocol was tuned per system after seeing the error against experiment, and the supplement says the ABFE arm's protocol was optimised on this same data set, so both physics baselines are optimistic.; The time column has no stated denominator or hardware, and three of its cells are ranges or bounds rather than values.; The series definition behind the 87 compounds rests on a citation that does not resolve in the supplement's reference list.; Four targets is a small sample on which to average.
comparison group: ml-vs-fep
comparison title: Machine learning against free energy calculation on the same series
stratum label: FEP+ benchmark, four-target subset
stratum order: 2
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-t12-abfe; uc-leadopt-20261022-eval-t12-bacpi; uc-leadopt-20261022-eval-t12-boltz-2; uc-leadopt-20261022-eval-t12-boltz-2-iptm; uc-leadopt-20261022-eval-t12-chemgauss4; uc-leadopt-20261022-eval-t12-fep-plus; uc-leadopt-20261022-eval-t12-fmo; uc-leadopt-20261022-eval-t12-gat; uc-leadopt-20261022-eval-t12-mm-pbsa; uc-leadopt-20261022-eval-t12-openfe
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fields: status; attributes.artifact_sha256; sha256: 6e31fabd2a46f1b867adbf58be440609b0060aca9e10abcfe9723259cef3bb46; record id: use-case-lead-optimisation-affinity-ranking; fields: status; name; description; facets; attributes.question; attributes.decision; attributes.inputs; attributes.output; attributes.exclusions; attributes.setting; attributes.clinical_scope; attributes.intended_users; sha256: 7432deaf6a9ac54d76ccc53abb33ca8b040bf88886f79afd081a08dd686511df
reason: First evidence for this use case: the only extracted protocol printing accuracy and compute cost side by side, independently reviewed against its pinned artifact on 2026-10-22.

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subject: Rank close analogues of a hit by binding affinity

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