{
  "abstract": "Pooled correlation, the metric usually reported when predicting mutation effects on protein–protein binding affinity (ΔΔG), mixes two different capabilities: placing targets on a common binding scale and ranking mutations correctly within a target. We formulate an exact between-/within-target covariance decomposition for PPI ΔΔG benchmarking, r = C_B + C_W, and apply it to matched, provenance-filtered evaluation sets. In a controlled comparison that changes only the split regime, on identical 7,523 mutation rows and identical model configurations, median pooled Pearson falls from 0.6844 under random splitting to 0.1508 under family-out-of-distribution evaluation, with ΔC_B = −0.4347 and ΔC_W = −0.1011; the between-target component accounts for 81.1% of the summed absolute component change. Target weighting changes how the components should be read: across five schemes C_B reverses sign in 40/109 configurations and in 34/109 even above |C_B| > 0.005, whereas C_W reverses in only 5/109. Fixed-K shortlist metrics are constrained by candidate-set geometry: a rank-linear discriminability surrogate is zero when n ≤ K and peaks near n* ≈ 20.5 for K = 10, and 40.8% of targets with n ≥ 5 fall in that degenerate regime. A matched retrospective ablation separated modifications that improve pooled scores only from those that also improve within-target ranking. External evidence from five methods is underpowered and its apparent cross-axis agreement is not robust to score orientation. Benchmarks should therefore report both components under declared weighting and candidate-set geometry.",
  "abstract_shown": true,
  "affiliations": [
    "Shanghai Jiao Tong University School of Medicine"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Le Yu",
      "orcid": "0009-0000-8807-3973"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5ns548s5t369v.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010846/v1",
  "fulltext_url": null,
  "id": "5ns548s5t369v",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010846/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Machine Learning in Bioinformatics"
    ]
  ],
  "openalex": "W7223174638",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010846/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/5ns548s5t369v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:24Z",
      "native_id": "10.26434/chemrxiv.15010846",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010846/v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Machine Learning in Bioinformatics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Disentangling Between-Target and Within-Target Signal in PPI Benchmarking",
  "title_text": "Disentangling Between-Target and Within-Target Signal in PPI Benchmarking",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:24Z",
  "url": "https://research.draconitic.com/paper/5ns548s5t369v",
  "venue": "ChemRxiv",
  "versions": []
}