Synthetic Applicability Domain (SynAD): Navigating Chemical Space for Reliable AI‐Driven Reaction Prediction

Organic synthetic chemistry has undergone a paradigm shift driven by breakthroughs in artificial intelligence (AI). Data-driven methods help accelerate hypothesis evaluation and reduce experimental trial-and-error efforts. However, its practical utility is constrained by the out-of-distribution (OOD) issue, where predictions usually fail when extrapolating to unseen reactions with new catalysts, substrates, or conditions. Here, we introduce SynAD (synthetic applicability domain), a machine learning framework for assessing the predictive capability of AI models trained with existing data. SynAD combines descriptors with model-adaptive distance metrics to automatically demarcate reliable and unreliable reactions. Validated on the Ullmann Ligand Dataset (ULD, >5000 reactions), SynAD a priori distinguishes predictable chemical space, resulting in a prediction accuracy of R² = 0.90 (at 12.3% coverage) from a baseline of R² = -0.21. This capacity to target reliable chemical space is consistently observed across 6 additional datasets. We also enable a SynAD score to quantify reaction class predictability, guiding experimental focus on OOD spaces. By defining model limits, SynAD provides a critical guardrail for chemists to trust AI, allocate resources strategically, and accelerate de novo discovery.