{
  "abstract": "Computer-assisted synthesis planning (CASP) software allows experimentalists to streamline setups and reduce unsuccessful experiments. CASP has a long history, yet remains an active research area. Although various tools have been developed and published, few have remained publicly available and continue to be supported. Those available at no-cost typically operate on generalized global databases, ignoring domain-or laboratory-specific constraints. Here, we present LAGER, a graph-grammar-based retrosynthesis engine built on the Double Pushout (DPO) formalism of the MØD framework. Retrosynthetic pathways are resolved by a state-based A∗ search with a weighted evaluation function. The path cost, g(n), penalizes pathway length and chemoselectivity risks from competing side reactions, whereas the heuristic h(n) estimates chemical distance to available reactants using fragment-based synthetic accessibility score (SAscore) to prioritize structurally simpler precursors and limit search-space expansion. LAGER is designed to accelerate synthesis planning for subsequent generations of a specific compound series by leveraging synthetic knowledge from previous generations. Its reaction library therefore comprises well-established reactions routinely used within a particular laboratory alongside readily available starting materials. We evaluate LAGER on a benchmark of 109 investigated inhibitors of elastase B (LasB), a key virulence factor of the Gram-negative bacterium Pseudomonas aeruginosa. With appropriate heuristic weighting, LAGER recovers the experimentally used pathway among Top-3 proposed pathways for up to 81% of targets. Importantly, deviation from the reported route did not imply chemical infeasibility: at least one chemically valid pathway was identified within the Top-3 candidates for 98% of targets. However, our results indicate limitations of SAscore as a chemical distance metric for targeted retrosynthesis, highlighting the need for more robust, computationally efficient alternatives. Comparison with AiZynthFinder using identical starting materials further demonstrated the importance of laboratory-specific reaction knowledge, as general reactiontemplate sets rarely reproduced the complete literature route for the investigated compounds and often included only a subset of the key transformations in the proposed pathways. Scientific Contribution. This work demonstrates the value of incorporating laboratory-specific reaction knowledge into automated retrosynthetic planning. Unlike general-purpose approaches, LAGER explicitly encodes domain-specific transformations within an interpretable graph-grammar-based search framework, providing a customizable approach for synthesis planning within a defined chemical space.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science & Interdisciplinary Center for Bioinformatics",
    "Leipzig University",
    "Competence Center for Scalable Data Services and Solutions Dresden/Leipzig (scaDS.AI)",
    "Helmholtz Centre for Infection Research (HZI)",
    "Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS)",
    "German Center for Infection Research (DZIF)",
    "PharmaScienceHub (PSH)",
    "Department of Pharmacy",
    "Saarland University",
    "School for Embedded and Composite Artificial Intelligence (SECAI",
    "Santa Fe Institute",
    "Department of Theoretical Chemistry",
    "University of Vienna",
    "Max Planck Institute for Mathematics in the Sciences",
    "Facultad de Ciencias",
    "Universidad National de Colombia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Arina Shelashen",
      "orcid": "0009-0005-3419-3810"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Thomas Gatter"
    },
    {
      "affs": [
        3,
        4,
        5,
        6
      ],
      "name": "Charlotte R. O’Donnell"
    },
    {
      "affs": [
        3,
        4,
        5,
        6,
        7,
        8
      ],
      "name": "Angeliki Vgenopoulou"
    },
    {
      "affs": [
        3,
        4,
        5,
        6,
        7,
        8
      ],
      "name": "Anna K. H. Hirsch"
    },
    {
      "affs": [
        0,
        1,
        2,
        9,
        10,
        11,
        12,
        13,
        14,
        15
      ],
      "name": "Peter F. Stadler"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/93txv4cy9yvq7.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010079/v1",
  "fulltext_url": null,
  "id": "93txv4cy9yvq7",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010079/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": "W7220904213",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010079/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/93txv4cy9yvq7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15010079",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010079/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Efficient Customizable Synthesis Planning with Graph Grammars, with an application to Elastase B inhibitor candidates",
  "title_text": "Efficient Customizable Synthesis Planning with Graph Grammars, with an application to Elastase B inhibitor candidates",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/93txv4cy9yvq7",
  "venue": "ChemRxiv",
  "versions": []
}