Reaction Template

Reaction templates are crucial for predicting and understanding chemical reactions, serving as building blocks for retrosynthesis and reaction pathway generation. Current research focuses on developing and improving machine learning models, including transformer-based networks and graph neural networks, often incorporating optimal transport methods or leveraging large pre-trained models on reaction databases to enhance accuracy and efficiency. These advancements aim to improve the prediction of reaction products, the design of novel synthesis routes, and the creation of comprehensive reaction databases, ultimately accelerating drug discovery and materials science.