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This paper presents a multi-agent LLM pipeline that automatically generates and verifies reaction rules for chemical synthesis, expanding a standard taxonomy from 68 to 14,073 classes without human curation, achieving 97.7% classification accuracy on unseen reactions.
This paper presents ConRetroBert, a dual encoder framework for template-based single-step retrosynthesis that uses contrastive pretraining and listwise ranking to improve template prediction accuracy, achieving up to 75.4% top-1 accuracy on the USPTO-50k benchmark while maintaining interpretability.