A large-scale reaction dataset of mechanistic pathways of organic reactions.

Autor: Chen S; School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Seoul, 08826, South Korea., Babazade R; Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon, 34141, South Korea., Kim T; Department of Chemistry, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon, 34141, South Korea., Han S; Department of Chemistry, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Daejeon, 34141, South Korea. sunkyu.han@kaist.ac.kr., Jung Y; School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Seoul, 08826, South Korea. yousung.jung@snu.ac.kr.; Institute of Engineering Research, Seoul National University, 1 Gwanak-ro, Seoul, 08826, South Korea. yousung.jung@snu.ac.kr.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2024 Aug 10; Vol. 11 (1), pp. 863. Date of Electronic Publication: 2024 Aug 10.
DOI: 10.1038/s41597-024-03709-y
Abstrakt: Understanding organic reaction mechanisms is crucial for interpreting the formation of products at the atomic and electronic level, but still remains as a domain of knowledgeable experts. The lack of a large-scale dataset with chemically reasonable mechanistic sequences also hinders the development of reliable machine learning models to predict organic reactions based on mechanisms as human chemists do. Here, we present a high-quality and the first large-scale reaction dataset, denoted as mech-USPTO-31K, with chemically reasonable arrow-pushing diagrams validated by synthetic chemists, encompassing a wide spectrum of polar organic reaction mechanisms. We envision this dataset curated by applying a simple and flexible method that automatically generates reaction mechanisms using autonomously extracted reaction templates and expert-coded mechanistic templates to become an invaluable tool to develop future reaction outcome prediction models and discover new reactions.
(© 2024. The Author(s).)
Databáze: MEDLINE
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