{
  "abstract": "Nano self-assembly organizes molecular components into bioactive nanoscale structures. Self-assembled nanoparticles (NAPs) derived from Chinese herbal formulas and applications such as anti-lung-cancer therapy demonstrate the substantial potential of self-assembly for nanomedicine discovery. Yet discovery still relies on costly wet-lab screening, while existing machine learning approaches lack standardized tasks, effective pairwise compatibility modeling, and public benchmarks with unified evaluation. To address these limitations, we formalize NSA prediction as a binary classification task for predicting self-assembly between molecular pairs and then establish NSA-Bench, the first public benchmark with curated molecular combinations, experimental conditions, self-assembly labels, and standardized evaluation protocols. We further develop NSA-Net, an interaction-aware multimodal framework that integrates complementary molecular evidence from graph topology, sequence semantics, and physicochemical descriptors to learn molecular-pair representations for self-assembly prediction. Extensive experiments on NSA-Bench show that NSA-Net achieves a ROC-AUC of $0.9470\\pm0.0112$ (Small) and $0.9492\\pm0.0062$ (Large). On the Small track, it surpasses the strongest machine-learning and graph-based baselines by 3.9 and 17.1 percentage points, respectively. Representation analyses reveal interpretable molecular characteristics associated with self-assembly prediction captured by the learned representations. Moreover, an NSA-Agent case study further demonstrates how NSA-Net predictions can support formulation refinement through experimental-condition-aware reasoning. Our code is available at https://github.com/developer-hq/NSA-Net.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.04278",
  "author_total": 12,
  "authors": [
    {
      "name": "Quan Hao"
    },
    {
      "name": "Mengyue Fan"
    },
    {
      "name": "Zifan Dong"
    },
    {
      "name": "Jianduo Zhao"
    },
    {
      "name": "Changhao Xiao"
    },
    {
      "name": "Shangqing Jiao"
    },
    {
      "name": "Hao Zhang"
    },
    {
      "name": "Yudong Wang"
    },
    {
      "name": "Fei Xia"
    },
    {
      "name": "Jigang Wang"
    },
    {
      "name": "Liguo Zhang"
    },
    {
      "name": "Chong Qiu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0sj34sbrv50s3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.04278",
  "fulltext_url": null,
  "id": "0sj34sbrv50s3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.04278",
  "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",
      "q-bio.QM"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7211918987",
  "pdf_url": "https://arxiv.org/pdf/2609.04278",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0sj34sbrv50s3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:19Z",
      "native_id": "2609.04278",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.04278"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Towards AI-Driven Nanomedicine Discovery: A Benchmark and Multimodal Learning Framework for Nano Self-Assembly Prediction",
  "title_text": "Towards AI-Driven Nanomedicine Discovery: A Benchmark and Multimodal Learning Framework for Nano Self-Assembly Prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:25Z",
  "url": "https://research.draconitic.com/paper/0sj34sbrv50s3",
  "venue": "arXiv",
  "versions": []
}