This paper investigates the comparative performance of two neural network architectures with graph input–output: the graph attention network (GAT) and the graph isomorphism network (GIN) for the prediction of molecular properties, i.e., regression and classification. Molecules are considered graphs where atoms constitute the nodes, and chemical bonds constitute the edges, thus allowing the networks to learn those structural features that seem important for bioactivity and pharmacokinetics. The graph attention network uses attention to put heavier weight on some neighboring atoms, whereas the graph isomorphism network is meant to capture graph structures with high discriminative power. The novelty of the present study is that it provides a comprehensive evaluation of these architectures across all datasets and tasks, ensuring the experimental setting is consistent for a fair comparison. Performance is measured through standard metrics, e.g., prediction error, classification accuracy, curve-based thresholding, while loss trend analysis ensures training stability. An interesting discovery was that the GIN is more suited for regression problems thanks to having very strong structural representation, whereas the GAT may have an edge in some classification problems due to its ability to dynamically weigh the importance of informative substructures. This comparative research paper thus provides insight into each method’s strengths and weaknesses and thus guides any researcher in choosing graph-based methods that are suitable for specific molecular prediction problems in drug design and cheminformatics.