Reaction Detection

Reaction detection encompasses the automated identification and analysis of chemical reactions from various data sources, aiming to accelerate chemical discovery and understanding. Current research focuses on developing machine learning models, including graph neural networks and sequence-to-sequence architectures, to predict reaction mechanisms, extract reaction information from literature and databases, and evaluate the plausibility of retrosynthetic routes. These advancements leverage large datasets and sophisticated algorithms to improve the accuracy and efficiency of reaction prediction and analysis, impacting fields like drug discovery and materials science through improved design and synthesis strategies. Furthermore, research extends beyond chemistry, exploring the detection of human reactions to stimuli like music using wearable sensors and signal processing techniques.