With the large-scale development of drugs, understanding the drug phase
behaviors in complex systems become increasingly important. Among them,
the solubility of drugs in biorelevant media needs to be urgently
understood. To address this challenge, new strategies based on machine
learning models are proposed. First, the strategy trains five machine
learning models based on fifteen molecular descriptors of the drug
molecular properties. The XGboost model was identified as the best
predictive model for predicting drug solubility performance in various
solvents. Next, the input feature vectors were expanded for machine
learning using the MACCS chemical fingerprint coupled with the XGboost
model. The MACCS chemical fingerprint coupled with the XGboost model has
significantly improved the prediction accuracy of drug solubility. This
finding demonstrates that the proposed strategy has solubility
prediction capability, which is expected to provide valid information
for drug development and drug solvent screening.