Reaction Yield Prediction

Reaction yield prediction aims to accurately forecast the outcome of chemical reactions, guiding chemists towards high-yielding synthesis routes and minimizing costly experimentation. Current research emphasizes addressing data imbalances, where low-yield reactions are overrepresented, by employing techniques like cost-sensitive re-weighting and focusing on few-shot learning methods. Advanced machine learning models, including differentiable random forests and multimodal transformers incorporating both molecular structures and calculated descriptors, are being developed and refined to improve prediction accuracy, particularly for high-yield reactions. This improved prediction capability promises to accelerate chemical discovery and optimize reaction design in various fields.