{
  "abstract": "Selective prediction with split conformal calibration promises a post-hoc fix for molecular property prediction: abstain on the least reliable compounds, ideally out-of-distribution chemistry, and the retained set becomes trustworthy without retraining. We audit this recipe for a gradient-boosted fingerprint model on lipophilicity (regression) and BBBP (classification) under a Bemis-Murcko scaffold split, holding the fitted model and evaluation molecules fixed while varying only the calibration fold (18 replicates). (i) On lipophilicity, coverage depends on calibration provenance: calibrating on held-out training-scaffold molecules falls below nominal (PICP 0.841 at a 0.90 target), while novel-scaffold calibration recovers it (0.904); on BBBP, coverage stays above nominal in every regime. (ii) For split conformalized quantile regression (CQR) the conformal correction is a constant, so conformalization does not change what is abstained on. (iii) The abstention score (CQR width; effectively ensemble variance on BBBP) tracks local label variance more than chemical novelty: controlling for local label variance largely explains away the novelty association (partial Spearman 0.067 and 0.047). (iv) At a fixed assay budget, gating on interval width or ensemble variance never improves hit precision over ranking by point prediction, at the pre-specified operating point or across 20 exploratory settings; a plain novelty gate shows small, uncorrected gains at the smallest budgets. (v) In an exploratory analysis, exact conformal selection exceeds its false discovery rate target under training-scaffold calibration (0.303 at q=0.2) and is consistent with it under novel-scaffold calibration, contributing a data-driven batch size rather than a better ranking. Conformalization earns its keep through guarantees whose validity hinges on calibration provenance, not by choosing what to abstain on.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.01512",
  "author_total": 2,
  "authors": [
    {
      "name": "Arnav Srivastava"
    },
    {
      "name": "Krishna Kassetty"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f52vta8s7e5nk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.01512",
  "fulltext_url": null,
  "id": "f52vta8s7e5nk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.01512",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.01512",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f52vta8s7e5nk.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.01512",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.01512"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Abstention Spends Its Budget on Local Label Variance More Than on Novelty: Auditing Conformal Calibration and Selective Prediction under Molecular Scaffold Shift",
  "title_text": "Abstention Spends Its Budget on Local Label Variance More Than on Novelty: Auditing Conformal Calibration and Selective Prediction under Molecular Scaffold Shift",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/f52vta8s7e5nk",
  "venue": "arXiv",
  "versions": []
}