Both GNNs and classical ML models were evaluated. Specifically, we explored nine different GNN architectures: graph convolutional network (GCN), graph attention network (GAT), message passing neural network (MPNN), AttentiveFP, Weave, and four variants of the graph isomorphic network (GIN) pretrained using different strategies: deep graph infomax, attribute masking, context prediction, and edge prediction. For classical ML models, we selected RF, support vector machine (SVM), and XGBoost 40 due to their popularity and established efficacy in cheminformatics. These models are among the