Molecular Graph Neural Network

Molecular graph neural networks (GNNs) leverage the graph-like structure of molecules to learn representations for predicting molecular properties and simulating chemical reactions. Current research emphasizes improving GNN expressiveness by incorporating structural similarity information between molecules, utilizing fragment-based inductive biases, and extending to hypergraphs to capture higher-order relationships beyond pairwise interactions. These advancements aim to enhance the accuracy and efficiency of molecular simulations and property predictions, impacting drug discovery, materials science, and other fields reliant on accurate molecular modeling.