##基于图神经网络的药物分子设计

This paper explores the application of Graph Neural Networks (GNNs) for automated drug molecule design. Traditional drug discovery relies heavily on empirical methods and serendipitous findings, often suffering from high costs and low success rates. We propose a novel approach that leverages the inherent structural information within molecules by representing them as graphs and utilizing GNNs to learn the complex relationships between molecular structure and biological activity. The core claim of this work is the ability to automatically design drug molecules using GNNs. The mechanism involves graph representation learning and generation, enabling the model to predict and generate novel molecules with desired properties. This research offers a new paradigm for drug design, potentially accelerating the discovery process and improving the efficiency of identifying promising drug candidates. The key contributions of this work are the demonstrated effectiveness of GNNs in capturing structural features and predicting biological activity, and the framework for automated molecule generation. Further research will focus on incorporating more sophisticated molecular representations and expanding the scope of target diseases. ---