Solvent-dependent reactivity is a
key aspect of synthetic science,
which controls reaction selectivity. The contemporary focus on new,
sustainable solvents highlights a need for reactivity predictions
in different solvents. Herein, we report the excellent machine learning
prediction of the nucleophilicity parameter N in
the four most-common solvents for nucleophiles in the Mayr’s
reactivity parameter database (R
2 = 0.93
and 81.6% of predictions within ±2.0 of the experimental values
with Extra Trees algorithm). A Causal Structure Property Relationship
(CSPR) approach was utilized, with focus on the physicochemical relationships
between the descriptors and the predicted parameters, and on rational
improvements of the prediction models. The nucleophiles were represented
with a series of electronic and steric descriptors and the solvents
were represented with principal component analysis (PCA) descriptors
based on the ACS Solvent Tool. The models indicated that steric factors
do not contribute significantly, because of bias in the experimental
database. The most important descriptors are solvent-dependent HOMO
energy and Hirshfeld charge of the nucleophilic atom. Replacing DFT
descriptors with Parameterization Method 6 (PM6) descriptors for the
nucleophiles led to an 8.7-fold decrease in computational time, and
an ∼10% decrease in the percentage of predictions within ±2.0
and ±1.0 of the experimental values.