The search for ML solubility models has received considerable attention in recent years, particularly with regard to drugs and drug-like compounds . Notably, Zhang et al. utilized data-driven predictions and 3D molecular representations to bridge the gap between computational models and experimental validation for quinone-like molecules. Chaka et al. enhanced the predictability of solubility by employing a graph neural network (GNN) named MolGAT . Because various ML algorithms were used across both descriptor-and graph-based models for predicting solubility, systematic research into the descriptors is called for to improve the accuracy of predictions, especially in medium-sized databases.