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

ABSTRACT: 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.

SUBMITTER: Chen S

PROVIDER: S-EPMC11316731 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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

Chen Shuan S Babazade Ramil R Kim Taewan T Han Sunkyu S Jung Yousung Y

Scientific data 20240810 1

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-U ...[more]

PMID: 39127730

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