Reaction Prediction

Reaction prediction, aiming to forecast the products of chemical reactions given reactants, is a crucial task in accelerating drug discovery and materials science. Current research heavily utilizes transformer-based models, including encoder-decoder architectures and GFlowNets, often leveraging large language models pre-trained on vast text corpora or fine-tuned with reaction datasets. These models are being improved through techniques like equivariant neural networks for enhanced accuracy and data efficiency, and by addressing limitations in evaluating model performance beyond simple metrics like top-k accuracy. Ultimately, advancements in reaction prediction promise to significantly streamline chemical synthesis planning and accelerate scientific discovery.