This project leverages graph neural networks (GNNs) to predict the antiviral potency of small molecules by analyzing their molecular structures and interactions. By learning complex patterns in chemical data, GNNs can identify promising drug candidates with high efficacy against viruses. The insights gained from this approach aim to accelerate antiviral drug discovery and development.