We describe a deep learning-based system for predicting chemical reactions and identifying experimentally-observed masses.

Calculated methyl anion affinities are known to correlate with experimentally determined Mayr E parameters for individual organic functional group classes but not between neutral and cationic organic electrophiles. We demonstrate that methyl anion affinities calculated with a solvation model (MAA*) give a linear correlation with Mayr E parameters for a broad range of functional groups. Methyl anion affinities (MAA*), plotted on the log scale of Mayr E, provide insights into the full range of electrophilicity of organic functional groups. On the Mayr E scale, the electrophilicity toward the methyl anion spans 180 orders of magnitude. Article pubs.acs.org/joc

To assess the diagnostic accuracy of cone beam computed tomography (CBCT) in comparison with conventional radiography for vertical root fractures, 50 of 100 teeth were subjected to vertical root fracture (VRF) and then placed in dry mandibles. 3D scans were obtained for all teeth, and conventional radiographs were used as control images. All the images were assessed by 6 observers, who determined the presence of root fractures by using a 5-point confidence rating scale. The mean area under the curve (Az) for CBCT was 0.91, and that for conventional radiography was 0.64. The difference between the modalities was statistically significant (P = 0.003). On the basis of interclass coefficient, inter-observer agreement for CBCT was 0/750, and that for conventional radiography was 0/637. Thus CBCT was shown to be significantly better than conventional periapical radiography for diagnosis of vertical root fractures in vitro.

Methyl cation affinities are calculated for the canonical nucleophilic functional groups in organic chemistry. These methyl cation affinities, calculated with a solvation model (MCA*), give an emprical correlation with the Ns N term from the Mayr equation under aprotic conditions when they are scaled to the Mayr reference cation (4-MeOC 6 H 4 ) 2 CH + (Mayr E = 0). Highly reactive anionic nucleophiles were found to give a separate correlation, while some ylides and phosphorus compounds were determined to give a poor correlation. MCA*s are estimated for a broad range of simple molecules representing the canonical functional groups in organic chemistry. On the basis of a linear correlation, we estimate the range of nucleophilicities of organic functional groups, ranging from a C−C bond to a hypothetical tert-butyl carbanion, toward the reference electrophile to be about 50 orders of magnitude.

There is a lack of scalable quantitative measures of reactivity that cover the full range of functional groups in organic chemistry, ranging from highly unreactive C−C bonds to highly reactive naked ions. Measuring reactivity experimentally is costly and time-consuming, and no single method has sufficient dynamic range to cover the astronomical size of chemical reactivity space. In previous quantum chemistry studies, we have introduced Methyl Cation Affinities (MCA*) and Methyl Anion Affinities (MAA*), using a solvation model, as quantitative measures of reactivity for organic functional groups over the broadest range. Although MCA* and MAA* offer good estimates of reactivity parameters, their calculation through Density Functional Theory (DFT) simulations is time-consuming. To circumvent this problem, we first use DFT to calculate MCA* and MAA* for more than 2,400 organic molecules thereby establishing a large data set of chemical reactivity scores. We then design deep learning methods to predict the reactivity of molecular structures and train them using this curated data set in combination with different representations of molecular structures. Using 10-fold cross-validation, we show that graph attention neural networks applied to a relational model of molecular structures produce the most accurate estimates of reactivity, achieving over 91% test accuracy for predicting the MCA* ± 3.0 or MAA* ± 3.0, over 50 orders of magnitude. Finally, we demonstrate the application of these reactivity scores to two tasks: (1) chemical reaction prediction and (2) combinatorial generation of reaction mechanisms. The curated data sets of MCA* and MAA* scores is available through the ChemDB chemoinformatics web portal at cdb.ics.uci.edu under Chemical Reactivities data sets.