A large-scale reaction dataset of mechanistic pathways of organic reactions
Shuan Chen, Ramil Babazade, Taewan Kim, Sunkyu Han, Yousung Jung
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.