{
  "abstract": "The informed discovery and inverse design of new molecules and materials is among the most active applications of data-driven procedures, such as statistical learning, to chemical problems. These approaches involve two key aspects: the dataset, and the exploration algorithms. Here, we cover both of these dimensions by introducing POP (Pnictogen Oracle Provider), a platform comprised by an extensive graph database and an optimization toolkit. The database contains almost 200,000 structures for phosphines, amines and arsines at different fidelity levels, explicitly including failed calculations and enforcing a topology based on substituent-level mutation operations. On the other hand, the software toolkit streamlines the application of optimization algorithms (search policies and predictive oracles) to such databases, pushing forward the development of benchmarks. We explore the baseline application of a varied set of such methods—evolutionary algorithms, Bayesian Optimization, LLM-based procedures and multifidelity approaches—to the optimization of a key property: the HOMO-LUMO gap across each of the three ligand families. In this manner, we reveal the relevance of graph topology in the guidance of these algorithms, as well as the performance differences these baseline methods depending on the particularities of each of the ligand families.",
  "abstract_shown": true,
  "affiliations": [
    "Instituto de Micro y Nanotecnología (IMN-CNM), CSIC, Isaac Newton 8, Tres Cantos, Madrid, Spain, 28760",
    "Department of Chemistry and Chemical Biology, McMaster University, 1280 Main Street West, Hamilton, Canada, L8S 4L8"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Diego Garay-Ruiz",
      "orcid": "0000-0003-0744-0562"
    },
    {
      "affs": [
        1
      ],
      "name": "Rodrigo A. Vargas-Hernández",
      "orcid": "0000-0002-5559-6521"
    },
    {
      "affs": [
        0
      ],
      "name": "Sergio Pablo-García",
      "orcid": "0000-0002-3327-9285"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ax6ryg5m7hmrn.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010180/v1",
  "fulltext_url": null,
  "id": "ax6ryg5m7hmrn",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010180/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7221088193",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010180/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ax6ryg5m7hmrn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15010180",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010180/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "The Pnictogen Oracle Provider: Multi-Fidelity Graph Landscapes To Benchmark Molecular Optimization",
  "title_text": "The Pnictogen Oracle Provider: Multi-Fidelity Graph Landscapes To Benchmark Molecular Optimization",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/ax6ryg5m7hmrn",
  "venue": "ChemRxiv",
  "versions": []
}