Reaction Prediction in chemistry AI refers to machine learning models that predict the products of chemical reactions given the reactants and conditions (forward prediction), or predict feasible reaction conditions, yields, and selectivity outcomes for proposed transformations. Reaction prediction complements retrosynthesis planning by validating proposed synthetic steps and predicting what will actually form when reagents are combined.

Why Reaction Prediction Matters in AI/ML: Reaction prediction enables in silico validation of synthetic routes proposed by retrosynthesis AI, predicting whether each step will produce the intended product with acceptable yield and selectivity, eliminating the need for experimental trial-and-error in route evaluation.

• Template-based forward prediction — Reaction templates (encoded as SMARTS transformations) are applied to reactants to generate candidate products; neural networks (Weisfeiler-Leman Difference Networks, GNNs) rank templates by likelihood, selecting the most probable transformation • Template-free forward prediction — The Molecular Transformer uses a sequence-to-sequence architecture to directly translate reactant SMILES to product SMILES, treating reaction prediction as machine translation; augmented SMILES and self-training improve accuracy to >90% top-1 • Reaction condition prediction — Given reactants and desired products, models predict optimal conditions: solvent, catalyst, temperature, and reagent quantities; this complements route planning by specifying how to execute each synthetic step • Yield prediction — ML models predict reaction yields (0-100%) from reactant structures and conditions: GNNs encode molecular graphs, and condition features (temperature, solvent, catalyst) are concatenated for yield regression; accuracy is typically ±15-20% MAE • Stereochemistry prediction — Predicting the stereochemical outcome (enantio/diastereoselectivity) of reactions is particularly challenging; specialized models predict major product stereochemistry for asymmetric reactions with 80-90% accuracy

Reaction prediction completes the AI-driven synthesis planning pipeline by computationally validating each step of proposed synthetic routes, predicting products, conditions, yields, and selectivity with accuracy approaching experimental reproducibility, transforming chemical synthesis from empirical trial-and-error into predictive, data-driven design.