{
  "abstract": "Small interfering RNAs (siRNAs) are short guide RNAs that recruit the RNA-induced silencing complex (RISC) to complementary target sites on messenger RNAs (mRNAs), triggering Ago2-mediated cleavage and gene silencing. siRNA design requires compact candidate sets that cover a target while preserving efficacy, specificity, and practical sequence constraints. Existing pipelines usually enumerate candidate windows, assign a canonical guide to each window, and then rank preconstructed siRNA--mRNA pairs. This has produced strong pairwise efficacy predictors, but leaves a candidate-construction gap: candidate positions and guide sequences are fixed before the model begins to rank them. We address this gap by decomposing siRNA candidate construction into two generative decisions: where to place candidates within an mRNA segment, and what constrained guide variants to consider at a candidate position. We instantiate this framework as siProGenA, using a Discrete Denoising Diffusion Probabilistic Model (D3PM) for mRNA-conditioned position proposal and a Bayesian Flow Network (BFN) for temperature-controlled guide generation. On 62 positive test segments, the diversity-aware final library reaches Hit@1 = 0.790 and Hit@5 = 0.903. In a measured-site controlled Stage~2 evaluation, seed- and cleavage-preserving variants outscore the canonical complement for 89.8% of measured sites, with supporting gains across additional computational scorers, random-mismatch controls, and biophysical diagnostics. Together, the results support a modular proposal--generation view of siRNA candidate construction for prioritizing compact candidate sets.",
  "abstract_shown": true,
  "affiliations": [
    "School of Engineering, Westlake University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Z. Ma"
    },
    {
      "name": "J. Zhou"
    },
    {
      "name": "R. Wang"
    },
    {
      "name": "Z. Deng"
    },
    {
      "name": "Z. Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cepathgkh7qeh.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.31.748303",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748303v1.full",
  "id": "cepathgkh7qeh",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748303v1",
  "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",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|RNA Interference and Gene Delivery"
    ]
  ],
  "openalex": "W7208763879",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748303v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cepathgkh7qeh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:28Z",
      "native_id": "10.64898/2026.08.31.748303",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748303v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "siProGenA: Generative siRNA Candidate Construction via Position Proposal and Guide Generation",
  "title_text": "siProGenA: Generative siRNA Candidate Construction via Position Proposal and Guide Generation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:28Z",
  "url": "https://research.draconitic.com/paper/cepathgkh7qeh",
  "venue": "bioRxiv",
  "versions": []
}