Molecular Graph

Molecular graphs represent molecules as networks of atoms (nodes) and bonds (edges), enabling computational analysis of their structure and properties. Current research heavily utilizes graph neural networks (GNNs), often combined with large language models (LLMs) and other techniques like diffusion models, to predict molecular properties, generate novel molecules, and analyze molecular dynamics. This work focuses on improving the expressiveness of molecular representations, incorporating higher-order interactions and multi-scale information, and developing more efficient and interpretable models. These advancements have significant implications for drug discovery, materials science, and other fields reliant on understanding molecular behavior.