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Abstract Proton transfers are fundamental steps in polar reaction mechanisms. We generated a large dataset of over 51 million kinetically plausible proton transfer steps between heteroatoms from about 8,000 acids and conjugate bases with experimental aqueous pK_as, spanning pK_avalues from −15 to +37. Rate factors were estimated at 25 °C using a simplified Eigen equation with pK_as but without statistical factors. Steps with estimated rate constants ≥ 10³M⁻¹s⁻¹were included in the final dataset. Additionally, 5,043 proton transfer steps from carbon acids to heteroatom bases were estimated using the Eigen-Bernasconi equation based on reported intrinsic rate constants and Brønsted β values. Carbon proton transfers with rate constants ≥ 10³ M⁻¹s⁻¹were added to the final dataset. Each entry was encoded in SMIRKS format with electron-flow specification for machine learning compatibility. Diversity of structure was prioritized over diversity of conditions; calculated rate constants are expected to be accurate in aqueous environments. This approach and dataset should prove valuable for training models to predict stepwise mechanistic pathways. 

Most online chemical reaction databases are not publicly accessible or are fully downloadable. These databases tend to contain reactions in noncanonicalized formats and often lack comprehensive information regarding reaction pathways, intermediates, and byproducts. Within the few publicly available databases, reactions are typically stored in the form of unbalanced, overall transformations with minimal interpretability of the underlying chemistry. These limitations present significant obstacles to data-driven applications including the development of machine learning models. As an effort to overcome these challenges, we introduce PMechDB, a publicly accessible platform designed to curate, aggregate, and share polar chemical reaction data in the form of elementary reaction steps. Our initial version of PMechDB consists of over 100,000 such steps. In the PMechDB, all reactions are stored as canonicalized and balanced elementary steps, featuring accurate atom mapping and arrow-pushing mechanisms. As an online interactive database, PMechDB provides multiple interfaces that enable users to search, download, and upload chemical reactions. We anticipate that the public availability of PMechDB and its standardized data representation will prove beneficial for chemoinformatics research and education and the development of data-driven, interpretable models for predicting reactions and pathways. PMechDB platform is accessible online at https://deeprxn.ics.uci.edu/pmechdb.