Reaction Path

Reaction path prediction focuses on computationally modeling the sequence of transformations a molecule undergoes during a chemical reaction, aiming to accurately predict reaction products and mechanisms. Current research heavily utilizes machine learning, employing graph neural networks, diffusion models, and reinforcement learning algorithms to predict reaction pathways and transition states, often leveraging large datasets of experimentally verified reactions. These advancements improve the efficiency and accuracy of reaction design and discovery, impacting fields like drug development and materials science by enabling faster and more targeted synthesis planning.