  There is a lack of scalable quantitative measures of reactivity for
functional groups in organic chemistry. Measuring reactivity experimentally is
costly and time-consuming and does not scale to the astronomical size of
chemical 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 dataset of chemical reactivity scores. We then design deep
learning methods to predict the reactivity of molecular structures and train
them using this curated dataset in combination with different representations
of molecular structures. Using ten-fold cross-validation, we show that graph
attention neural networks applied to informative input fingerprints produce the
most accurate estimates of reactivity, achieving over 91% test accuracy for
predicting the MCA* plus-minus 3.0 or MAA* plus-minus 3.0, over 50 orders of
magnitude. Finally, we demonstrate the application of these reactivity scores
to two tasks: (1) chemical reaction prediction; (2) combinatorial generation of
reaction mechanisms. The curated dataset of MCA* and MAA* scores is available
through the ChemDB chemoinformatics web portal at www.cdb.ics.uci.edu.
