Graph to Graph

Graph-to-graph (G2G) models address the challenge of transforming one graph representation into another, finding applications in diverse fields like retrosynthesis prediction in chemistry and linguistic structure modeling. Current research focuses on developing G2G architectures based on transformers and diffusion models, often incorporating techniques like iterative refinement and self-training to improve accuracy and efficiency. These advancements are significantly impacting fields requiring complex structural transformations, enabling more accurate predictions and potentially automating complex tasks in chemistry and other domains.