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

Key points

• The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors.1
• 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.1
• 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).1
• 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).1

Sources (1)

• [1]Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 06:10 AM

  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.

  The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors.

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