Graphormer Reactivity Model

Graphormer is a type of graph neural network that leverages the transformer architecture's attention mechanism to process graph-structured data, aiming to improve the efficiency and accuracy of graph learning tasks. Current research focuses on enhancing Graphormer's scalability and expressiveness through techniques like virtual connections, subgraph integration, and incorporating external information such as textual descriptions of chemical reactions. These advancements are improving performance across diverse applications, including molecular modeling, 3D reconstruction, and trajectory prediction, demonstrating the model's broad utility in scientific and engineering domains.