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Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

Nature, version of record; Europe PMC JATS full text for PMC12727541 · Peer-reviewed

DOI: 10.1038/s41586-025-09721-5

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Results

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ValueMetricModel or configurationDatasetEvaluation
5.0 percentSuccess rate (AlphaFold3 filter)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH computational success rate against H1 stem, AlphaFold3 filter
5.0 percentSuccess rate (AlphaFold3 filter)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH computational success rate against H1 stem, AlphaFold3 filter
3.5 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH computational success rate against H1 stem, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0
(3.5) percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0; value printed in parentheses because it repeats the 96-design row from the same design campaign)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH computational success rate against H1 stem, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0
7.5 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH computational success rate against H1 stem, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1
(7.5) percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1; value printed in parentheses because it repeats the 96-design row from the same design campaign)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH computational success rate against H1 stem, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1
11.5 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH computational success rate against H1 stem, fine-tuned RoseTTAFold2 filter, hotspot proportion 1
(11.5) percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 1; value printed in parentheses because it repeats the 96-design row from the same design campaign)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH computational success rate against H1 stem, fine-tuned RoseTTAFold2 filter, hotspot proportion 1
5.0 percentSuccess rate (AlphaFold3 filter)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target IL-7 Receptor αFine-tuned RFdiffusion VHH computational success rate against IL7R, AlphaFold3 filter
12.5 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target IL-7 Receptor αFine-tuned RFdiffusion VHH computational success rate against IL7R, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0
27.0 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target IL-7 Receptor αFine-tuned RFdiffusion VHH computational success rate against IL7R, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1
34.5 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target IL-7 Receptor αFine-tuned RFdiffusion VHH computational success rate against IL7R, fine-tuned RoseTTAFold2 filter, hotspot proportion 1
0.0* percentSuccess rate (AlphaFold3 filter)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target RSV-F Site IIIFine-tuned RFdiffusion VHH computational success rate against RSV site 3, AlphaFold3 filter
1.0 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target RSV-F Site IIIFine-tuned RFdiffusion VHH computational success rate against RSV site 3, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0
3.0 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target RSV-F Site IIIFine-tuned RFdiffusion VHH computational success rate against RSV site 3, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1
25.0 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target RSV-F Site IIIFine-tuned RFdiffusion VHH computational success rate against RSV site 3, fine-tuned RoseTTAFold2 filter, hotspot proportion 1
0.0 percentSuccess rate (AlphaFold3 filter)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target SARS-CoV-2 RBDFine-tuned RFdiffusion VHH computational success rate against SARS-CoV-2 RBD, AlphaFold3 filter
11.0 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target SARS-CoV-2 RBDFine-tuned RFdiffusion VHH computational success rate against SARS-CoV-2 RBD, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0
23.5 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target SARS-CoV-2 RBDFine-tuned RFdiffusion VHH computational success rate against SARS-CoV-2 RBD, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1
28.0 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target SARS-CoV-2 RBDFine-tuned RFdiffusion VHH computational success rate against SARS-CoV-2 RBD, fine-tuned RoseTTAFold2 filter, hotspot proportion 1
27.5 percentSuccess rate (AlphaFold3 filter)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target TcdBFine-tuned RFdiffusion VHH computational success rate against TcdB, AlphaFold3 filter
27.5 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target TcdBFine-tuned RFdiffusion VHH computational success rate against TcdB, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.0
33.5 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target TcdBFine-tuned RFdiffusion VHH computational success rate against TcdB, fine-tuned RoseTTAFold2 filter, hotspot proportion 0.1
41.0 percentSuccess rate (fine-tuned RoseTTAFold2 filter, hotspot proportion 1)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target TcdBFine-tuned RFdiffusion VHH computational success rate against TcdB, fine-tuned RoseTTAFold2 filter, hotspot proportion 1
1 countCount (SPR-confirmed binders; initial screening method SPR)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH design against H1 stem, E. coli expression and single-concentration SPR
1% percentHit rate (SPR-confirmed binders out of designs screened; initial screening method SPR)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH design against H1 stem, E. coli expression and single-concentration SPR
20 countCount (SPR-confirmed binders; initial screening method YSD)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH design against H1 stem, yeast surface display with SPR confirmation
0.2% percentHit rate (SPR-confirmed binders out of designs screened; initial screening method YSD)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target Influenza HemagglutininFine-tuned RFdiffusion VHH design against H1 stem, yeast surface display with SPR confirmation
0 countCount (SPR-confirmed binders; initial screening method SPR)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target IL-7 Receptor αFine-tuned RFdiffusion VHH design against IL7R, E. coli expression and single-concentration SPR
0% percentHit rate (SPR-confirmed binders out of designs screened; initial screening method SPR)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target IL-7 Receptor αFine-tuned RFdiffusion VHH design against IL7R, E. coli expression and single-concentration SPR
1 countCount (SPR-confirmed binders; initial screening method YSD)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target RSV-F Site IIIFine-tuned RFdiffusion VHH design against RSV site 3, yeast surface display with SPR confirmation
0.01% percentHit rate (SPR-confirmed binders out of designs screened; initial screening method YSD)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target RSV-F Site IIIFine-tuned RFdiffusion VHH design against RSV site 3, yeast surface display with SPR confirmation
5 countCount (SPR-confirmed binders; initial screening method YSD)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target SARS-CoV-2 RBDFine-tuned RFdiffusion VHH design against SARS-CoV-2 RBD, yeast surface display with SPR confirmation
0.06% percentHit rate (SPR-confirmed binders out of designs screened; initial screening method YSD)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target SARS-CoV-2 RBDFine-tuned RFdiffusion VHH design against SARS-CoV-2 RBD, yeast surface display with SPR confirmation
2 countCount (SPR-confirmed binders; initial screening method SPR)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target TcdBFine-tuned RFdiffusion VHH design against TcdB, E. coli expression and single-concentration SPR
2% percentHit rate (SPR-confirmed binders out of designs screened; initial screening method SPR)Fine-tuned RFdiffusion VHH design with fine-tuned RoseTTAFold2 filtering at hotspot proportion 1Bennett VHH design target TcdBFine-tuned RFdiffusion VHH design against TcdB, E. coli expression and single-concentration SPR

Claims

Statements this source supports, each with where in the source it was checked.

  • Baseline for the de novo binder design use case: Baseline Directed evolution, display selection or grafting (Adaptyv preprint Sections 2.3 and 3.2 (EGF and cetuximab as positive controls; cetuximab at 9.94 nM); Bennett 2025 Discussion (contrast with yeast display selection from random libraries); AlphaProteo Table 1 caption ('Binders derived from selection-based methods, such as antibodies and nanobodies, are not considered here'))
  • CDR-H3 mean sequence similarity of H1 stem binders (96-design row): Cdr h3 mean sequence similarity of binders 33.4% (Supplementary Methods Table 6, row 'H1 stem' (Designs screened = 96), column 'CDR-H3 mean sequence similarity of binders (%)')
  • CDR-H3 mean sequence similarity of H1 stem binders (9000-design row): Cdr h3 mean sequence similarity of binders (33.4%) (Supplementary Methods Table 6, row 'H1 stem' (Designs screened = 9000), column 'CDR-H3 mean sequence similarity of binders (%)')
  • CDR-H3 mean sequence similarity of IL7R binders (96-design row): Cdr h3 mean sequence similarity of binders N/A (Supplementary Methods Table 6, row 'IL7R' (Designs screened = 96), column 'CDR-H3 mean sequence similarity of binders (%)')
  • CDR-H3 mean sequence similarity of RSV site 3 binders (9000-design row): Cdr h3 mean sequence similarity of binders N/A (Supplementary Methods Table 6, row 'RSV site 3' (Designs screened = 9000), column 'CDR-H3 mean sequence similarity of binders (%)')
  • CDR-H3 mean sequence similarity of SARS-CoV-2 RBD binders (9000-design row): Cdr h3 mean sequence similarity of binders 20.4% (Supplementary Methods Table 6, row 'SARS-CoV-2 RBD' (Designs screened = 9000), column 'CDR-H3 mean sequence similarity of binders (%)')
  • CDR-H3 mean sequence similarity of TcdB binders (96-design row): Cdr h3 mean sequence similarity of binders 22.2% (Supplementary Methods Table 6, row 'TcdB' (Designs screened = 96), column 'CDR-H3 mean sequence similarity of binders (%)')
  • Only 9% of the ordered Bennett VHH designs have AlphaFold3 ipTM above 0.6: Filter power Results 'Improved oracles increase success rate', verbatim: 'We found that the AlphaFold3 interface predicted template modelling (ipTM) score, a measure of model confidence over the interface, is predictive of binding success (area under the curve = 0.86; Extended Data Fig. 10a,b). Overall, only 9% of our ordered VHH designs have an ipTM > 0.6, suggesting that success rate will be improved by incorporation of an ipTM filter.' And for the scFv libraries: 'Only 4% of the initial design library has ipTM > 0.85, whereas 5 out of the 6 experimentally confirmed designs pass this threshold.' The paper also states that the fine-tuned RoseTTAFold2 filter's 'filtering power ... is limited (at least with the settings used; providing 100% of interface hotspots)' and that this 'probably accounts for the low experimental success rates'. (Results 'Improved oracles increase success rate')
  • OrthoRep affinity-maturation fold improvement for H1 stem (96-design row): Orthorep fold improvement in affinity N/A (Supplementary Methods Table 6, row 'H1 stem' (Designs screened = 96), column 'OrthoRep fold improvement in affinity')
  • OrthoRep affinity-maturation fold improvement for H1 stem (9000-design row): Orthorep fold improvement in affinity ~1000x (Supplementary Methods Table 6, row 'H1 stem' (Designs screened = 9000), column 'OrthoRep fold improvement in affinity')
  • OrthoRep affinity-maturation fold improvement for IL7R (96-design row): Orthorep fold improvement in affinity N/A (Supplementary Methods Table 6, row 'IL7R' (Designs screened = 96), column 'OrthoRep fold improvement in affinity')
  • OrthoRep affinity-maturation fold improvement for RSV site 3 (9000-design row): Orthorep fold improvement in affinity N/A (Supplementary Methods Table 6, row 'RSV site 3' (Designs screened = 9000), column 'OrthoRep fold improvement in affinity')
  • OrthoRep affinity-maturation fold improvement for SARS-CoV-2 RBD (9000-design row): Orthorep fold improvement in affinity ~1000x (Supplementary Methods Table 6, row 'SARS-CoV-2 RBD' (Designs screened = 9000), column 'OrthoRep fold improvement in affinity')
  • OrthoRep affinity-maturation fold improvement for TcdB (96-design row): Orthorep fold improvement in affinity ~100x (Supplementary Methods Table 6, row 'TcdB' (Designs screened = 96), column 'OrthoRep fold improvement in affinity')
  • Relevance of uc-binder-20261022-protocol-bennett-insilico-af3-success to "Which computational design workflow produces binders that work when they are made and tested?": Assessed by Bennett VHH computational success rate under an AlphaFold3 filter (uc-binder-20261022-source-bennett-si: Supplementary Methods Table 6, column 'Success rate (%) (AF3)')
  • Relevance of uc-binder-20261022-protocol-bennett-insilico-rf2-success to "Which computational design workflow produces binders that work when they are made and tested?": Assessed by Bennett VHH computational success rate under the fine-tuned RoseTTAFold2 filter (uc-binder-20261022-source-bennett-si: Supplementary Methods Table 6, columns 'Success rate (%) (RF2, hotspot proportion = 1 / 0.1 / 0.0)')
  • Relevance of uc-binder-20261022-protocol-bennett-vhh-ecoli-spr-96 to "Which computational design workflow produces binders that work when they are made and tested?": Assessed by Bennett VHH design, E. coli expression and single-concentration SPR screen of 96 designs (uc-binder-20261022-source-bennett-si: Supplementary Methods Table 6, rows with 'Initial screening method' = SPR; uc-binder-20261022-source-bennett: Supplementary Methods Table 6, rows with 'Initial screening method' = SPR)
  • Relevance of uc-binder-20261022-protocol-bennett-vhh-yeast-display-9000 to "Which computational design workflow produces binders that work when they are made and tested?": Assessed by Bennett VHH design, yeast surface display screen of 9,000 designs with SPR confirmation (uc-binder-20261022-source-bennett-si: Supplementary Methods Table 6, rows with 'Initial screening method' = YSD; uc-binder-20261022-source-bennett: Supplementary Methods Table 6, rows with 'Initial screening method' = YSD)
  • Summary of the de novo binder design evidence: Summary Four studies, pinned here as seven artifacts, carry measured outcomes for de novo binder design, and none of them answers which workflow to use, because no paper runs two design methods and a conventional comparator on the same targets with one assay and one hit definition. The literature pass behind this evidence found nine candidate sources with measured outcomes; the five that were not retrieved are recorded as gaps rather than as evidence of what they would show. What the measured evidence shows is that the target matters more than the method. AlphaProteo's yeast-display success rate across eight targets runs from 0% over 54 designs on TNF-alpha to 88% over 94 designs on BHRF1, at a binding-signal threshold of 0.2 (1.3 on IL-17A). BindCraft reports an average experimental success rate of 46.3% over twelve targets with a range of 10% to 100%, built from per-target counts as small as 2 binders from 7 designs on Bet v1 and as large as 6 from 6 on the SpCas9 REC1 domain. The same family of methods applied to antibody variable domains is two orders of magnitude worse: fine-tuned RFdiffusion gives 2% over 96 designs on TcdB, 0% over 96 on IL-7 receptor alpha, and 0.01% to 0.2% over 9,000 designs per target by yeast display. On influenza H1 stem the same design campaign gives 1% over 96 designs by SPR and 0.2% over 9,000 by display, so the screening method moves the rate as much as the target does. Nearly all of this is author-reported by the group that built the method, which also chose the targets, ran the assay and chose which designs to test. The exception is the Adaptyv EGFR competition, where a third-party laboratory expressed and measured designs from many entrants on one target and one assay: 2.5% of 201 round-1 designs bound (printed elsewhere in the same paper as 3%), and 53 of 400 round-2 designs bound, printed as 13%. That source cannot be turned into a per-method rate: design methods are the entrants' own declarations, the organisers hand-picked 101 of 201 round-1 and 300 of 400 round-2 designs for diversity, and the three binding thresholds behind the figure counts are never defined. Affinities reached are better than the hit rates suggest. AlphaProteo's best measured binder was 0.082 nM on IL-7 receptor alpha by HTRF; BindCraft's best ranged from 5 nM on CbAgo to 5.7 micromolar on CrSAS-6; the best de novo design in the Adaptyv competition reached 82 nM and the winning optimised cetuximab fragment 1.21 nM, against 9.94 nM for the cetuximab positive control measured on the same instrument. Three things stop these numbers from ranking methods. The hit definitions are different endpoints, not different thresholds on one endpoint. The denominators are small and chosen, and the designs tested are the top picks of each method's own filter, so design and selection are coupled: changing only AlphaProteo's filter moved its PD-L1 rate from 9.6% over 94 designs to 16.1% over 31, and the Bennett authors attribute their low antibody rates partly to a filter they describe as weak. And the spread within one method across targets is wider than any plausible difference between methods. No conventional comparator is run anywhere in this evidence, so there is no measurement of what a designed binder is being compared against. (AlphaProteo Table 1, Table S2 and Sections S1.2.1, S2 and S4.1; BindCraft Introduction and Results prose; Bennett Supplementary Methods Table 6; Adaptyv Sections 2.3, 2.4, 3.1, 3.2, 4.1, 4.2 and 4.3)
  • Two Bennett VHH design targets have no outcome row: Targets without printed outcome Supplementary Methods Table 5 rows: HIV Env (2NY7), SARS-CoV-2 RBD (6M0J), RSV-F Site I (7LVW), RSV-F Site III (7LUC), Influenza Hemagglutinin (5VLI), TcdB (6C0B), IL-7 Receptor alpha (3DI3). Section 6.8 states 'We designed VHHs against 6 targets in total', which does not match the seven rows. Table 6's caption states 'For 5 targets for which de novo VHH binders were identified, details are provided', and Table 6 has rows only for TcdB, IL7R, H1 stem (twice), SARS-CoV-2 RBD and RSV site 3. No outcome is printed for RSV-F site I or HIV Env, and none is inferred here. (Supplementary Methods Tables 5 and 6; Section 6.8)

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Pipelines (1)

Protocols (4)

Datasets (7)

Use cases (1)

Evidence

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29 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
attributes.artifact_sha256
d7876bc73c47f2cb3fcb0fa1ef917c89c9b67750a22f6402bf515f978cd1564a
Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

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https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12727541/fullTextXML
Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

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10.1038/s41586-025-09721-5
Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

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1 values
  • artifact sha256
    d7876bc73c47f2cb3fcb0fa1ef917c89c9b67750a22f6402bf515f978cd1564a
    message
    The main text says 'at lower throughput with Escherichia coli expression and single-concentration surface plasmon resonance (SPR; 95 designs per target; TcdB, IL-7Ra and influenza haemagglutinin)', but Supplementary Methods Table 6 prints 96 designs screened for TcdB, IL7R and H1 stem. Table 6's value is recorded because it is the chosen table; the main text figure is 95.
    review date
    2026-10-22
    source id
    uc-binder-20261022-source-bennett
    source locator
    Results paragraph 2; Supplementary Methods Table 6, column 'Designs screened'
Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

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Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

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  • Developer-reported: the authors fine-tuned RFdiffusion, chose the targets and ran the screens.
  • The main text states '95 designs per target' for the SPR-screened campaigns, while Supplementary Methods Table 6 prints 96 designs screened for each of them.
Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

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Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

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peer_reviewed
Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

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Version: Nature, version of record; Europe PMC JATS full text for PMC12727541
Retrieved: 2026-10-22T11:55:00Z

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Source artifact SHA-256: d7876bc73c47f2cb3fcb0fa1ef917c89c9b67750a22f6402bf515f978cd1564a

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Retrieval method
Europe PMC REST fullTextXML endpoint, flattened to text for reading.
Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

Original source ↗

No field-specific location recorded

Version: Nature, version of record; Europe PMC JATS full text for PMC12727541
Retrieved: 2026-10-22T11:55:00Z

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Source has a recorded evidence concern. Consult its source page before using the claim.

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Source artifact SHA-256: d7876bc73c47f2cb3fcb0fa1ef917c89c9b67750a22f6402bf515f978cd1564a

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Inspected artifact

Retrieved at
2026-10-22T11:55:00Z
Source metadata		Atomically accurate de novo design of antibodies with RFdiffusion (Bennett et al., Nature 2025)

Original source ↗

No field-specific location recorded

Version: Nature, version of record; Europe PMC JATS full text for PMC12727541
Retrieved: 2026-10-22T11:55:00Z

catalogued

No individual claim review recorded

Source has a recorded evidence concern. Consult its source page before using the claim.

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Source artifact SHA-256: d7876bc73c47f2cb3fcb0fa1ef917c89c9b67750a22f6402bf515f978cd1564a

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

How to read the evidence

Sources and history

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

Primary source retrieved and hashed for the 2026-10-22 de novo binder design extraction (issue #176).

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Technical metadata and extraction receipts

Stable ID: uc-binder-20261022-source-bennett

areas
proteins-complexes; protein-structure
contexts
research
artifact sha256
d7876bc73c47f2cb3fcb0fa1ef917c89c9b67750a22f6402bf515f978cd1564a
artifact url
https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12727541/fullTextXML
doi
10.1038/s41586-025-09721-5
evidence concerns
artifact sha256: d7876bc73c47f2cb3fcb0fa1ef917c89c9b67750a22f6402bf515f978cd1564a; message: The main text says 'at lower throughput with Escherichia coli expression and single-concentration surface plasmon resonance (SPR; 95 designs per target; TcdB, IL-7Ra and influenza haemagglutinin)', but Supplementary Methods Table 6 prints 96 designs screened for TcdB, IL7R and H1 stem. Table 6's value is recorded because it is the chosen table; the main text figure is 95.; review date: 2026-10-22; source id: uc-binder-20261022-source-bennett; source locator: Results paragraph 2; Supplementary Methods Table 6, column 'Designs screened'
licence
CC-BY-4.0
limitations
Developer-reported: the authors fine-tuned RFdiffusion, chose the targets and ran the screens.; The main text states '95 designs per target' for the SPR-screened campaigns, while Supplementary Methods Table 6 prints 96 designs screened for each of them.
media type
application/xml
publication status
peer_reviewed
retrieval method
Europe PMC REST fullTextXML endpoint, flattened to text for reading.
retrieved at
2026-10-22T11:55:00Z
review
artifact sha256: d7876bc73c47f2cb3fcb0fa1ef917c89c9b67750a22f6402bf515f978cd1564a; method: source-hash-verification; independent-cell-check; ai-assisted-source-review; method note: Re-downloaded from Europe PMC (HTTP 200, 169,840 bytes) and the SHA-256 recomputed and matched. Own flattening of the JATS XML. The main text's '95 designs per target' was confirmed verbatim and the string '96 designs' does not occur, so the conflict with Supplementary Methods Table 6 is real.; note: Reviewed in docs/reviews/use-cases/binder-2026-10-22.md. Automated AI review of the source transcription only; it is not human scientific review and nothing here was reproduced experimentally.; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC12727541/fullTextXML; reviewed at: 2026-10-22T15:00:00Z; reviewer: claude; reviewer note: Claude review agent, independent of the extraction agent; no human review claimed
source locator
Results paragraph 2 and 'Improved oracles increase success rate'
url
https://doi.org/10.1038/s41586-025-09721-5
version
Nature, version of record; Europe PMC JATS full text for PMC12727541
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