{
  "abstract": "The functionality of catalysts, enzymes, and supramolecular assemblies emerges not from individual molecules alone, but from the subtle interplay between multiple components arranged in complex systems. Designing such systems is a grand challenge: the combinatorial explosion of possible chemical compositions and spatial arrangements makes brute-force exploration infeasible, while many current generative approaches remain limited to isolated molecules. In this work, we introduce a hierarchical generative optimization framework that overcomes this barrier by coupling a genetic algorithm for configurational search with a generative model for molecular design. This closed-loop approach enables simultaneous refinement of geometry and composition, efficiently steering discovery toward systems with targeted functionality. As a proof of concept, we design catalytic environments for the Claisen rearrangement of p-tolyl ether by optimizing surrounding components around a fixed reference transition-state geometry. Despite this constraint during the search phase, post-hoc validation via Climbing-Image Nudged Elastic Band calculations, with the environment held fixed, indicates a reduction of the activation barrier by about 30%. Beyond this example, the framework applies to systems in which a well-defined central structure is stabilized by non-covalently interacting components, such as supramolecular hosts and local catalytic environments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Rhyan Barrett"
    },
    {
      "name": "Robin Curth"
    },
    {
      "name": "Julia Westermayr",
      "orcid": "0000-0002-6531-0742"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1c5jwjh7e1fjq.bib",
  "cited_by": 0,
  "doi": "10.1186/s13321-026-01313-3",
  "fulltext_url": null,
  "id": "1c5jwjh7e1fjq",
  "issns": [
    "1758-2946"
  ],
  "landing_url": "https://link.springer.com/10.1186/s13321-026-01313-3",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7154476643",
  "pdf_url": "https://link.springer.com/content/pdf/10.1186/s13321-026-01313-3.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/1c5jwjh7e1fjq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:48Z",
      "native_id": "10.1186/s13321-026-01313-3",
      "source": "crossref",
      "url": "https://link.springer.com/10.1186/s13321-026-01313-3"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Hierarchical generative modeling for the design of multi-component systems",
  "title_text": "Hierarchical generative modeling for the design of multi-component systems",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:48Z",
  "url": "https://research.draconitic.com/paper/1c5jwjh7e1fjq",
  "venue": "Journal of Cheminformatics",
  "versions": []
}