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RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis — arXiv:2609.38484

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.38484
License (as archived): CC0-1.0
Archived: 2026-10-07T13:45:19.740Z
SHA-256 of the archived text: c5f61b3b1bcf494cef0836476b297a6700f12994bea2858d69bf932d72885199
Stable URL of this page: https://metraipolis.com/documento/dc_3d2e9cd652b8

How to cite

RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis — arXiv:2609.38484 — arXiv (en), https://arxiv.org/abs/2609.38484 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3d2e9cd652b8 (Metraipolis archive dc_3d2e9cd652b8, sha256 c5f61b3b1bcf494cef0836476b297a6700f12994bea2858d69bf932d72885199)

BibTeX

@misc{dc3d2e9cd652b8,
  title = {RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis — arXiv:2609.38484},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.38484},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3d2e9cd652b8; sha256 c5f61b3b1bcf494cef0836476b297a6700f12994bea2858d69bf932d72885199},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.38484. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Retrosynthesis enables the discovery of viable synthetic routes to target molecules. It plays a central role in modern drug discovery and materials design. Retrosynthesis involves molecular graph transformations that can change both connectivity and graph size. These transformations may introduce reactant components absent from the target while revising the product-derived structure. To model these transformations, we propose RetroGEF, a flow-based generative model for single-step retrosynthesis. Starting from the target molecule, it constructs possible reactants by adding atoms and changing bonds in the molecular graph. RetroGEF models molecular transformations and changes in graph size within the same generative process, rather than relying on a fixed-size graph canvas. It learns this process directly from product--reactant pairs without requiring a prescribed edit order. Experiments on representative retrosynthesis benchmarks demonstrate that RetroGEF achieves state-of-the-art performance.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-01 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Quantitative Methods (q-bio.QM).
Pagina del lavoro: https://arxiv.org/abs/2609.38484 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T13:18:10.214Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).