"Supplementary Data for Paper 'Graph Neural Network-Guided Discovery and Biological Evaluation of Novel AKT1 Inhibitors for Triple-Negative Breast Cancer'"

This dataset supports the study on identifying novel AKT1 inhibitors for triple-negative breast cancer (TNBC) using advanced Graph Neural Networks (GNNs). The AKT1 protein plays a crucial role in the PI3K/AKT/mTOR signaling pathway, making it a pivotal target for therapeutic development. Our work addresses the limitations of current AKT1 inhibitors, such as poor efficacy and high toxicity, by leveraging GNN architectures to predict novel scaffold compounds.