Efficient Customizable Synthesis Planning with Graph Grammars, with an application to Elastase B inhibitor candidates

@misc{shelashen2026efficient,
  title = {{Efficient Customizable Synthesis Planning with Graph Grammars, with an application to Elastase B inhibitor candidates}},
  author = {Arina Shelashen and Thomas Gatter and Charlotte R. O’Donnell and Angeliki Vgenopoulou and Anna K. H. Hirsch and Peter F. Stadler},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15010079/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010079/v1},
  note = {CC BY},
}

TY  - UNPB
TI  - Efficient Customizable Synthesis Planning with Graph Grammars, with an application to Elastase B inhibitor candidates
AU  - Arina Shelashen
AU  - Thomas Gatter
AU  - Charlotte R. O’Donnell
AU  - Angeliki Vgenopoulou
AU  - Anna K. H. Hirsch
AU  - Peter F. Stadler
PY  - 2026
DA  - 2026/10/08
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15010079/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010079/v1
AB  - 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.
ER  -