{
  "abstract": "Predicting mixture properties is a key challenge across domains, yet current deep-learning designs approach it blindly: they largely reuse the single-molecule recipe, compressing each molecule into a vector and predicting the mixture’s property from these vectors. We find that mixtures, as components multiply, depend increasingly on precise topological information. We therefore advocate a new design: model the mixture directly as a unified atomic system with complete and precise molecular topology. A simple instantiation of this design reaches or approaches the best published baselines across diverse domains, and indicates that the failures of existing vector-based models on some tasks stem from the same thing: every molecular vector is a map, yet the mixture is the territory.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "Xiao Yang",
      "orcid": "0000-0002-0159-1434"
    },
    {
      "name": "Guanghao Song"
    },
    {
      "name": "Zihan Xu"
    },
    {
      "name": "Yanan Yang"
    },
    {
      "name": "Baojiang He"
    },
    {
      "name": "Lingbo Ji"
    },
    {
      "name": "Xiankuan Huo"
    },
    {
      "name": "Jun Hu"
    },
    {
      "name": "Zhan Qu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0fj98hd96gkky.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15006353/v6",
  "fulltext_url": null,
  "id": "0fj98hd96gkky",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006353/v6",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7215110953",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15006353/v6",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0fj98hd96gkky.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15006353",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15006353/v6"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Less is more in mixture modeling: every molecular vector is a map, yet the mixture is the territory",
  "title_text": "Less is more in mixture modeling: every molecular vector is a map, yet the mixture is the territory",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/0fj98hd96gkky",
  "venue": "ChemRxiv",
  "versions": []
}