{
  "abstract": "Antisense oligonucleotides (ASOs) are a powerful class of drugs with the potential to treat a wide range of human diseases. However, the prediction of ASO efficacy remains challenging, as large-scale and costly experimental screens are typically required to identify optimal candidates for a specific therapeutic target. To address this challenge, we compiled ASO Atlas, a database comprising 188,521 RNase H-mediated ASO sequences targeting 334 unique genes with corresponding knockdown efficacy measurements extracted from published patents. Using ASO Atlas, we trained OligoAI, a deep learning model capable of jointly modelling RNA target context, ASO sequence, sugar and backbone chemistries, and dosage to predict in vitro efficacy. We experimentally validated OligoAI by targeting KCNT2, achieving a 5.72-fold reduction in screening effort compared to random selection. ASO Atlas provides the first systematic resource to rigorously evaluate hypotheses regarding key parameters in ASO design, including sequence composition, chemical modifications, and target region selection. Both ASO Atlas and OligoAI have been made freely accessible through an online web-tool with the aim of facilitating the accelerated optimisation of ASO design.",
  "abstract_shown": true,
  "affiliations": [
    "Big Data Institute and Centre for Human Genetics, University of Oxford, Oxford OX3 7LF, UK",
    "Department of Paediatrics, University of Oxford, Oxford OX3 7TY, UK",
    "Institute of Developmental and Regenerative Medicine, IMS-Tetsuya Nakamura Building, Old Road Campus, Oxford OX3 7TY, UK",
    "Department of Paediatrics, University of Oxford, OX3 7TY Oxford, United Kingdom",
    "MRC Nucleic Acid Therapy Accelerator, Harwell Research and Innovation Campus, Didcot OX11 0FA, UK",
    "MRC Nucleic Acid Therapy Accelerator, Harwell Research and Innovation Campus, OX11 0FA Didcot, United Kingdom",
    "Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA",
    "Big Data Institute and Centre for Human Genetics, University of Oxford, OX 7LF Oxford, United Kingdom",
    "Oxford Harrington Rare Disease Centre, IDRM, Old Road Campus, Oxford OX3 7TY, UK",
    "Department of Paediatrics, University of Oxford, OX3 7TY Oxford, United Kingdom 2MRC Nucleic Acid Therapy Accelerator, Harwell Research and Innovation Campus, OX11 0FA",
    "Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94178, USA",
    "New York Genome Center, New York, NY 10013, USA"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Barney Hill",
      "orcid": "0000-0002-7417-1393"
    },
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        4,
        5
      ],
      "name": "Maisie R. Jaques"
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    {
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        4,
        5
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      "name": "R. Nair",
      "orcid": "0000-0001-9007-5740"
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        0,
        6,
        7
      ],
      "name": "Nicola Whiffin",
      "orcid": "0000-0003-1554-6594"
    },
    {
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        2,
        8,
        9
      ],
      "name": "Matthew J. A. Wood",
      "orcid": "0000-0002-5436-6011"
    },
    {
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        10,
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        11,
        9
      ],
      "name": "Stephan Sanders",
      "orcid": "0000-0001-9112-5148"
    },
    {
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        4,
        5
      ],
      "name": "Peter L. Oliver",
      "orcid": "0000-0003-3347-2461"
    },
    {
      "affs": [
        1,
        2,
        3
      ],
      "name": "Alyssa C. Hill",
      "orcid": "0000-0002-4767-5780"
    },
    {
      "affs": [
        1,
        2,
        8,
        3
      ],
      "name": "Carlo Rinaldi",
      "orcid": "0000-0002-8307-6638"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bygs0840zxyf7.bib",
  "cited_by": 9,
  "doi": "10.1016/j.omtn.2026.103004",
  "fulltext_url": null,
  "id": "bygs0840zxyf7",
  "issns": [
    "2162-2531"
  ],
  "landing_url": "https://doi.org/10.1016/j.omtn.2026.103004",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "1312|RNA Interference and Gene Delivery"
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  "openalex": "W4415713988",
  "pdf_url": null,
  "pmcid": "PMC13427547",
  "pmid": "42542598",
  "published": "2026-07-07",
  "publisher": "Cell Press",
  "ris": "https://research.draconitic.com/paper/bygs0840zxyf7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:48Z",
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      "url": "https://doi.org/10.1016/j.omtn.2026.103004"
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  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|RNA Interference and Gene Delivery",
      "subfield": "molecular-biology"
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  ],
  "title": "Accurately modeling RNase H-mediated antisense oligonucleotide efficacy",
  "title_text": "Accurately modeling RNase H-mediated antisense oligonucleotide efficacy",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:07Z",
  "url": "https://research.draconitic.com/paper/bygs0840zxyf7",
  "venue": "Molecular Therapy — Nucleic Acids",
  "versions": []
}