{
  "abstract": "High-throughput drug screening uses inexpensive primary screens to select compounds for more costly follow-up dose-response experiments, which estimate compound potency (pXC50) by determining the concentration required to produce a specified biological effect. However, many promoted compounds fail to yield a reportable potency, even when activity is subsequently confirmed. We investigated whether inexpensive three-point primary-screening data could predict whether a more costly 11-point dose-response experiment would yield a reportable pXC50. Using a dataset of 32,971 compound-assay pairs with three-point primary-screening and 11-point dose-response measurements, we trained random-forest models to predict successful potency quantification. Our model achieved an AUROC of 0.939 +/- 0.002 and generalized to previously unseen chemical scaffolds and assay-mechanism families. We found that primary-screen response features were more predictive of dose-response quantifiability than molecular structure-based features. Successful potency quantification was also strongly associated with primary-screen response amplitude. Across eight chronological test releases, the model retrospectively retained 90% of reportable potencies while selecting only 55.7% of historically promoted compound-assay pairs for follow-up profiling. These findings suggest that predicting potency quantifiability from primary-screening data could improve the efficiency of compound prioritization and reduce unnecessary dose-response experiments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.26538",
  "author_total": 1,
  "authors": [
    {
      "name": "Sean Lim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3kgeyc0w6j827.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.26538",
  "fulltext_url": null,
  "id": "3kgeyc0w6j827",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.26538",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.BM"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7204534891",
  "pdf_url": "https://arxiv.org/pdf/2608.26538",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3kgeyc0w6j827.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:21Z",
      "native_id": "2608.26538",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.26538"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "arxiv:q-bio.BM",
      "subfield": "biophysics"
    }
  ],
  "title": "Predicting Dose-Response Success from Primary Screening Data",
  "title_text": "Predicting Dose-Response Success from Primary Screening Data",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/3kgeyc0w6j827",
  "venue": "arXiv",
  "versions": []
}