{
  "abstract": "High-throughput screening (HTS) assays are central to early-stage drug discovery but are often limited by extreme data sparsity, as primary screens typically use only a single replicate per test substance. This sparsity makes conventional machine-learning performance metrics, such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC), difficult to estimate empirically because they require adequately sized labeled samples. Here, we introduce a model-based framework that derives these classification metrics from the strictly standardized mean difference (SSMD), a well-established HTS effect-size parameter. Under a Gaussian equal-variance assumption, we derive closed-form relationships linking SSMD to Youden-optimal sensitivity and specificity, and sensitivity at a preset specificity, yielding explicit estimators and exact confidence intervals from the noncentral t-distribution, even under single-replicate designs. Unlike classical statistical power, which approaches 1 as sample size grows regardless of how small the true non-zero difference between group means is, the SSMD-derived sensitivity converges to a finite population value that reflects the true degree of separation between two groups, making it a more meaningful and stable performance measure for hit selection. We demonstrate the utility of this framework in a hepatitis C virus primary siRNA screen comprising approximately 22,000 single-replicate measurements, showing that SSMD, AUROC, and sensitivity-based thresholds yield equivalent and interpretable hit sets. This work bridges classical HTS statistics and machine-learning evaluation theory, providing a statistically principled, reproducible way to estimate classification performance in ultra-low-replication screening workflows.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: SLAS Discovery 44 (2026), 100340",
  "arxiv": "2608.07609",
  "author_total": 1,
  "authors": [
    {
      "name": "Xiaohua Douglas Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/es7p1m5e77a3j.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.07609",
  "fulltext_url": null,
  "id": "es7p1m5e77a3j",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.07609",
  "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.AI"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2608.07609",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/es7p1m5e77a3j.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:52Z",
      "native_id": "2608.07609",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.07609"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Hit Selection Using SSMD-Based Machine Learning Performance Metrics in High-Throughput Screening Assays",
  "title_text": "Hit Selection Using SSMD-Based Machine Learning Performance Metrics in High-Throughput Screening Assays",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:58Z",
  "url": "https://research.draconitic.com/paper/es7p1m5e77a3j",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1016/j.slasd.2026.100340",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}