Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

We present a group contribution method
(SoluteGC) and a machine
learning model (SoluteML) to predict the Abraham solute parameters,
as well as a machine learning model (DirectML) to predict solvation
free energy and enthalpy at 298 K. The proposed group contribution
method uses atom-centered functional groups with corrections for ring
and polycyclic strain while the machine learning models adopt a directed
message passing neural network. The solute parameters predicted from
SoluteGC and SoluteML are used to calculate solvation energy and enthalpy
via linear free energy relationships. Extensive data sets containing
8366 solute parameters, 20,253 solvation free energies, and 6322 solvation
enthalpies are compiled in this work to train the models. The three
models are each evaluated on the same test sets using both random
and substructure-based solute splits for solvation energy and enthalpy
predictions. The results show that the DirectML model is superior
to the SoluteML and SoluteGC models for both predictions and can provide
accuracy comparable to that of advanced quantum chemistry methods.
Yet, even though the DirectML model performs better in general, all
three models are useful for various purposes. Uncertain predicted
values can be identified by comparing the three models, and when the
3 models are combined together, they can provide even more accurate
predictions than any one of them individually. Finally, we present
our compiled solute parameter, solvation energy, and solvation enthalpy
databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide
public access to our final prediction models through a simple web-based
tool, software packages, and source code.