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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. 

Description / Abstract: Organic radical reactions are crucial in many areas of chemistry, including synthetic, biological, and atmospheric chemistry. We develop a predictive framework based on the interaction of molecular orbitals that operates on mechanistic-level radical reactions. Given our chemistry-aware model, all predictions are provided with different levels of interpretability. Our models are trained and evaluated using the RMechDB database of radical reaction steps. Our model predicts the correct orbital interaction and products for 96% of the test reactions in RMechDB. By chaining these predictions, we perform a pathway search capable of identifying all intermediates and byproducts of a radical reaction. We test the pathway search on two classes of problems in atmospheric and polymerization chemistry. RMechRP is publicly available online at https://deeprxn.ics.uci.edu/rmechrp/. 

A zipped file containing:51M_Heteroatom.csv - 51M proton transfer steps from heteroatom acids to heteroatom bases, with SMIRKS, calculated log k1, and pKas,5KCarbonPT.csv - 5K proton transfer steps from carbon acids to heteroatom bases, with SMIRKS, calculated log k1, and pKas, Brønsted β values, statistical factors (qB, pB, qC, pC) intrinsic rate constants (ko),49ExperimentalCarbonPT.csv - 49 proton transfers from heteroatom acids to carbon bases in SMIRKS format, with experimentally measured log k1, and literature references.51M_heteroatom_raw – a subfolder with two files containing lists of 7.6K heteroatomic acids and bases in SMILES format with the acidic and basic atoms labeled, with pKas, literature references: Acid.csv, ConBase.csv100_Heteroatom.csv - A representative sample set of 100 out of the 51M proton transfer steps100K_Heteroatom.csv - A representative sample set of 100,000 out of the 51M proton transfer stepscarbon_acid_raw – a subfolder containing a list of intrinsic rate constants for carbon acids in SMILES format, with statistical factors (Carbon_Acids.csv) and a subfolder named Bases containing seven lists of heteroatom base classes (ArO-.csv, R2NH.csv, R3N.csv, “RCO2- and ArCO2-.csv”, RNH2.csv, RO-.csv and RS-.csv). Lists of heteroatom bases are in SMILES format, sectioned by class and with statistical factors, selected from the Heteroatom set 

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. 

We introduce RMechDB, an open-access platform for aggregating, curating, and distributing reliable data about elementary radical reaction steps for computational radical reaction modeling and prediction. RMechDB contains over 5,300 elementary radical reaction steps, each with a single transition state at or around room temperature. These elementary step reactions are manually curated plausible arrow-pushing steps for organic radical reactions. The steps were taken from a variety of sources. Over 2,000 mechanistic steps were extracted from textbooks and/or constructed from research publications. Another 3,000 were taken from gas-phase atmospheric reactions of isoprene and other organic molecules on the MCM (Master Chemical Mechanism) Web site. Reactions are encoded in the SMIRKS format with accurate atom mapping and annotations for arrow-pushing mechanisms. At its core, RMechDB consists of a database schema with an online interactive search interface and a request portal for downloading the raw form of elementary step reactions with their metadata. It also offers an interface for submitting new reactions to RMechDB and expanding the data set through community contributions. Although there are several applications for RMechDB, it is primarily designed as a central platform of radical elementary steps with a unified and structured representation. We believe that this open access to this data and platform enables the extension of data-driven models for chemical reaction predictions and other chemoinformatics predictive tasks.