The goal of this project is to use Graph Neural Networks (GNNs) to predict drug potency against drug targets (e.g., an ion channel protein). Specifically, we want to compare the performance of traditional QSAR (Quantitative Structure-Activity Relationship) models with GNN-based models. Traditional QSAR methods rely on handcrafted molecular descriptors—numerical representations of molecular properties—to predict a compound’s activity. Since GNNs offer a unique approach by treating molecules as graphs (preserving connectivity, spatial relationships, and chemical properties), this would allow the model to learn molecular features directly from their structures (there have been papers on this type of application).
To make this project more biologically relevant, if time permits, we could extend the QSAR-GNN models by incorporating protein structure information into the learning process.