We investigated the prediction for the solvation free energies of anions using two different approaches. The first approach directly trains a GNN model on D2A-dGsolv-anion/train for predicting ∆G * solv (A -). The second approach trains separate GNN models on D2A-dGgas/train and D2A-pKa/train, and utilizes Chung's ∆G * solv (AH) model. These pK a , ∆G o acid, gas (AH), and ∆G * solv (AH) model predictions are then combined using Equation 1 to predict ∆G * solv (A -). Because the underlying data do not need to contain the same solutes, this method could take advantage of larger data corpuses during training. For instance, in this work, the pK a dataset is larger than the ∆G o acid, gas (AH) dataset. Alternatively, individual parts of each model could be swapped out with higher-accuracy QM methods. In water, which is the most significant solvent in the dataset, the model predicts with an RMSE of 3.85 kcal mol -1 , demonstrating reasonable accuracy for most organic anions tested, but having the tendency to vastly over-or underpredict for a small set of outliers. Such errors tended to be higher in the more negative solvation free energy range. We observed Although carboxylates are widely represented in the dataset, the model errors in water for such species are around 4 kcal mol -1 . Upon further investigation of the data, we found that the MAE for carboxylates with only one carboxylic acid group was just 1.6 kcal mol -1 , whereas the error for compounds with two or more carboxyl groups was 6.1 kcal mol -1 .