For decades, predicting the products of a chemical reaction relied on intuition built from years of laboratory experience. Today, machine learning models trained on curated reaction datasets can propose likely outcomes in seconds, giving chemists a powerful starting point for synthesis planning.
The most successful approaches combine graph-based molecular representations with transformer architectures, allowing models to capture both structural context and reaction-level patterns. Crucially, transparent evaluation metrics make it possible to understand when a prediction should be trusted.
At ChemAiX, we believe AI works best as a collaborator rather than a replacement. Our research tools pair every model suggestion with the underlying evidence, so researchers stay firmly in control of the scientific reasoning.