Different data representations and models for predicting CO 2 solubility in deep eutectic solvents (DESs) were proposed and analyzed. GNN models were tested with different architectures and molecular descriptors. Considering all the metrics proposed in this work, the GCN did not outperform the baseline model and the RF. Also, high deviations were obtained in the final model, mainly attributed to the increasing number of parameters involved. Furthermore, only the node features are passed through the GCN without considering edge information that could be highly relevant. The information obtained from the disconnected graph can be concatenated with the global features and hydrogen bond information in the MLP layer to increase the accuracy of the GCN.