{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/found-in-translation-predicting-outcomes-of","title":"\"Found in Translation\": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models","arxiv_id":"1711.04810","date":"2017-11-13","proceeding":null,"authors":["Philippe Schwaller","Theophile Gaudin","David Lanyi","Costas Bekas","Teodoro Laino"],"abstract":"There is an intuitive analogy of an organic chemist's understanding of a\ncompound and a language speaker's understanding of a word. Consequently, it is\npossible to introduce the basic concepts and analyze potential impacts of\nlinguistic analysis to the world of organic chemistry. In this work, we cast\nthe reaction prediction task as a translation problem by introducing a\ntemplate-free sequence-to-sequence model, trained end-to-end and fully\ndata-driven. We propose a novel way of tokenization, which is arbitrarily\nextensible with reaction information. With this approach, we demonstrate\nresults superior to the state-of-the-art solution by a significant margin on\nthe top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1%\nwithout relying on auxiliary knowledge such as reaction templates. Also, 66.4%\naccuracy is reached on a larger and noisier dataset.","url_abs":"http://arxiv.org/abs/1711.04810v2","url_pdf":"http://arxiv.org/pdf/1711.04810v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"found-in-translation-predicting-outcomes-of","repo_url":"https://github.com/ManzoorElahi/organic-chemistry-reaction-prediction-using-NMT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.04810","atlas_url":"https://app.syntology.ai/?focus=1711.04810","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}