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Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction — arXiv:2610.19640

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.19640
License (as archived): CC0-1.0
Archived: 2026-10-15T14:17:42.180Z
SHA-256 of the archived text: 50565119a354fdc6a014ab3e25e7b0bc0661d8a004ee380d14b4eac9b72cd363
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How to cite

Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction — arXiv:2610.19640 — arXiv (en), https://arxiv.org/abs/2610.19640 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d01d68cf024c (Metraipolis archive dc_d01d68cf024c, sha256 50565119a354fdc6a014ab3e25e7b0bc0661d8a004ee380d14b4eac9b72cd363)

BibTeX

@misc{dcd01d68cf024c,
  title = {Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction — arXiv:2610.19640},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.19640},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d01d68cf024c; sha256 50565119a354fdc6a014ab3e25e7b0bc0661d8a004ee380d14b4eac9b72cd363},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.19640. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors. We present enviGraph, a framework that generates and curates synthetic reaction data, uses these data to pretrain a graph-to-sequence predictor, and fine-tunes the predictor on observed environmental reactions for product and recursive pathway prediction. Applying inherited environmental transformation rules to structures from PubChem, NORMAN SusDat and EPA DSSTox, followed by curation, produces 776,851 canonical parent-product pairs for pretraining. We compare this dataset with three United States Patent and Trademark Office (USPTO) reaction datasets: USPTO-50K, USPTO-MIT and USPTO-Stereo. We evaluate enviGraph and the sequence-based enviFormer using ten folds split by pathway on the EAWAG Biocatalysis/Biodegradation Database (BBD), regulatory pesticide biodegradation data (Soil) and activated-sludge biodegradation data (Sludge). For both predictors, synthetic pretraining gives the highest BBD mean precision-recall area under the curve (PR-AUC) among the evaluated pretraining datasets for single-generation and multi-generation prediction. enviGraph achieves BBD single-generation PR-AUC of $0.202 \pm 0.050$ with synthetic pretraining, compared with $0.169 \pm 0.065$ for the retrospectively selected strongest USPTO condition, USPTO-Stereo (mean $\pm$ sample standard deviation across folds). Its exact BBD top-1 and top-5 accuracies with synthetic pretraining are 0.355 and 0.594. The strongest USPTO conditions give higher PR-AUC means than synthetic pretraining in most Soil and Sludge comparisons. enviGraph has higher Soil multi-generation PR-AUC means than enviFormer under each of the four main pretraining datasets, and with synthetic pretraining it also leads enviFormer on BBD single-generation PR-AUC and exact top-1 and top-5 accuracy.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.19640 — 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 2026-10-15T13:53:35.624Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).