Designing solvent systems using self-evolving solubility databases and graph neural networks
Yeonjoon Kim, Hojin Jung, Sabari Kumar, Robert S. Paton, Seonah Kim · Chemical Science · 2023
for the test set) is quantitatively useful in exploring Linear Free Energy Relationships between reaction rates and solvation free energies for 11 organic reactions. Our model also accurately predicted the partition coefficients of lignin-derived monomers and drug-like molecules. While there is room for expanding solubility predictions to transition states, radicals, charged species, and organometallic complexes, this approach will be attractive to predictive chemistry areas where experimental, computational, and other heterogeneous data should be combined.