Solvent-Specific Featurization for Predicting Free Energies of Solvation through Machine Learning

A featurization algorithm based on functional class fingerprints has been implemented within the DeepChem machine learning framework. It is based on descriptors more appropriate for solvation, taking into account intermolecular properties, and has been used in the prediction of free energies of solvation. Tests carried out on solvents with a range of polarity from the FreeSolv and MNSol data sets have shown slightly better accuracy than the commonly used topology-based extended connectivity fingerprint algorithm for hydration free energies. However, improvement was not as significant as hoped and less clear for less polar solvents suggesting that further solvent-specific descriptors may need to be taken into consideration.