{
  "abstract": "Self-driving laboratories can autonomously execute iterative design-make-test-analyse cycles, but molecular optimisation presents a distinctive challenge: selecting which compound to elaborate determines not only the next experiment, but also the chemical space accessible in subsequent rounds. Conventional molecular Bayesian optimisation typically evaluates compounds without considering how their selection changes what can subsequently be synthesised. Here, we introduce Lookahead Bayesian Optimisation (LABO), which instead values candidate molecules according to the predicted value of the synthetically accessible chemical space they unlock, enabling optimisation to account for future experimental opportunities. We evaluate LABO in simulated five-round molecular optimisation campaigns across 30 lead-like starting compounds and five kinase targets. LABO outperformed established molecular optimisation strategies from the literature across all 30 optimisation landscapes, as well as random, greedy and Bayesian optimisation controls. LABO was shown to improve ligand-lipophilicity efficiency by 1.25 units over random selection, equivalent to approximately 18-fold greater potency at equal lipophilicity, and by 0.34 units (2.2-fold) over greedy elaboration. These results demonstrate a general principle for autonomous molecular optimisation: under synthetic constraints, the value of a molecule depends not only on its predicted properties, but on the future experiments that selecting it makes possible. By explicitly incorporating this future experimental value into molecular selection, LABO provides a framework for planning successive rounds of molecular growth in self-driving laboratories.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Engineering",
    "University Park Nottingham"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Connor J. Taylor",
      "orcid": "0000-0002-4002-5937"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/856ka74pgawwd.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009896/v1",
  "fulltext_url": null,
  "id": "856ka74pgawwd",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009896/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",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7220388601",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009896/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/856ka74pgawwd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15009896",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009896/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Lookahead Bayesian Optimisation for Molecular Growth in Self-Driving Laboratories",
  "title_text": "Lookahead Bayesian Optimisation for Molecular Growth in Self-Driving Laboratories",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/856ka74pgawwd",
  "venue": "ChemRxiv",
  "versions": []
}