{
  "abstract": "Target discovery in functional genomics remains largely manual and time-consuming, lacking systematic tools for efficient and reproducible gene-level hypothesis generation. We introduce AutoScreen, an AI co-scientist system supporting target discovery through both Pre-screen Design, which constructs perturbation libraries de novo from free-text research descriptions, and Post-screen Analysis, which re-ranks experimental screen hits by integrating statistical scores with biological context. AutoScreen leverages the complementary strengths of multiple specialist agents that perform deep research into multi-modal evidence, information restructuring, parallel searches across >26 biomedical databases, evidence synthesis, and target review to provide transparent, explainable gene prioritization with pipeline provenance. Across 320 genome-scale CRISPR screens as expert-curated benchmarks, AutoScreen achieved a ~19% increase in validated-hit recovery among its top 100 predictions, relative to the strongest agent baseline, and required a 1.2-fold smaller library to recover the same number of hits at the top-500 reference point. AutoScreen reached mean average precision more than two orders-of-magnitude above random baseline. Further, we validated AutoScreen in cancer immune-evasion case studies focusing on natural killer (NK) and T-cell therapeutics. AutoScreen recovered NK-resistance genes that were initially lower-ranked in a leukemia screen, moving MUC1, PDPN, and LRRC15 from raw ranks of 118, 81, 1384 to 5, 44, 659, respectively. In follow-up tumor killing assay with primary human NK cells, individual perturbation validated all three hits successfully. Next, in prospective benchmarking across two cytotoxic T-cell-killing screens, AutoScreen recovered 77.1% of ground-truth hits called by the consensus of two gold-standard analysis pipelines (FDR<0.1), achieving 8% improvement over the strongest general-purpose LLM agent baseline. Finally, we re-analyzed >500 public datasets to construct the AutoScreen Resource Hub, a growing knowledge base of pre-computed, annotated reports for CRISPR screens, RNA-seq differential expression, and gene-level UK Biobank genome-wide association studies. This agentic AI approach enables real-time genomics benchmark construction that are continuously updated, expanded, for evaluating frontier AI co-scientists. Overall, AutoScreen enables AI-powered target discovery to be more auditable, scalable, extensible, and reproducible.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pathology, Department of Genetics, Cancer Biology Program, Stanford University, Stanford, CA 94305, USA"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 24,
  "authors": [
    {
      "name": "Y. Qu"
    },
    {
      "name": "X. Liu"
    },
    {
      "name": "X. Wang"
    },
    {
      "name": "M. Chen"
    },
    {
      "name": "X. Luo"
    },
    {
      "name": "L. Lyu"
    },
    {
      "name": "M. Yin"
    },
    {
      "name": "J. Hui"
    },
    {
      "name": "D. Yin"
    },
    {
      "name": "R. Dinesh"
    },
    {
      "name": "L. Qiu"
    },
    {
      "name": "K. Huang"
    },
    {
      "name": "H. Wang"
    },
    {
      "name": "S. Tong"
    },
    {
      "name": "H. Cousins"
    },
    {
      "name": "R. Feng"
    },
    {
      "name": "O. Martinez"
    },
    {
      "name": "J. Zhang"
    },
    {
      "name": "T. Chen"
    },
    {
      "name": "R. Altman"
    },
    {
      "name": "J. Leskovec"
    },
    {
      "name": "A. Regev"
    },
    {
      "name": "M. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "L. Cong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0y54kdj5494py.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.06.749678",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.06.749678v1.full",
  "id": "0y54kdj5494py",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.06.749678v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioengineering"
    ],
    [
      "openalex",
      "1312|CRISPR and Genetic Engineering"
    ]
  ],
  "openalex": "W7212131218",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.06.749678v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0y54kdj5494py.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:52Z",
      "native_id": "10.64898/2026.09.06.749678",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.06.749678v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics",
  "title_text": "AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:52Z",
  "url": "https://research.draconitic.com/paper/0y54kdj5494py",
  "venue": "bioRxiv",
  "versions": []
}