📝 Abstract:
Graph Neural Networks (GNNs) have gained significant attention in drug discovery due to their ability to model graph-structured biological and chemical data, such as molecular graphs, drug–drug interaction networks, and drug–target interaction networks. Despite their success, practical adoption of GNNs is often limited by implementation complexity, lack of standardized benchmarking, and reduced generalization under distribution shifts across datasets and experimental conditions. This thesis addresses these challenges through two complementary contributions. First, a no-code framework (GNN-NCF) is introduced to support reproducible GNN model construction, hyperparameter optimization, training, evaluation, and inference across multiple drug discovery tasks. The framework enables systematic benchmarking of popular GNN architectures, including ChebConv, GCNConv, GATConv, GINConv, and SageConv, on representative datasets from the Open Graph Benchmark and additional drug–target interaction settings. Benchmarking results demonstrate that GNN performance is strongly task-dependent and sensitive to dataset properties such as class imbalance, graph density, and chemical diversity. Second, to improve robustness under domain shift in drug–target interaction prediction, this thesis proposes GRADDA-DTI, a gradient-aware domain adaptation method that combines multi-level embedding alignment (MMD and CORAL) with dynamic loss weighting (UPGrad). Experiments show that GRADDA-DTI improves crossdomain transfer performance on benchmark datasets. Overall, this work advances accessible and robust GNN-based workflows for drug discovery, supporting more reliable and reproducible computational screening.