Leveraging the Synergy between Mechanistic Insight and Machine Learning for Predictive Structure–Performance Relationships in Organic Synthesis

Conspectus Understanding how molecular structure determines reaction performance is a central objective of physical organic chemistry and an increasingly important challenge in data-driven organic synthesis. As the frontiers of synthetic chemistry continue to advance, predictive structure–performance relationship modeling is expected not only to reproduce known reaction outcomes but also to guide new chemical design. In this Account, we summarize our efforts to integrate mechanistic insight with machine learning across data construction, model development, and discovery applications. At the data level, mechanistic understanding can help define reaction spaces and learning targets, allowing automated density functional theory calculations and literature-derived databases to become chemically consistent learning resources. This idea is illustrated by computed selectivity data sets for radical C–H functionalization and Cu(II)-mediated radical bond formation as well as literature databases for asymmetric hydrogenation of olefins and N,N′-dioxide/metal-catalyzed Michael additions. At the modeling level, mechanistic knowledge can be embedded into reaction representations and model architectures. Transition-state-based reaction encoding introduces pathway-specific stereochemical information into enantioselectivity prediction, while steric- and electronics-embedded molecular graphs incorporate physical organic effects directly into molecular deep learning. At the discovery level, mechanistic relatedness provides a basis for selecting historical data and transferring structure–performance relationships into low-data chemical space. This mechanism-guided transfer learning formalizes how chemists reuse related precedents, enabling few-shot catalyst ligand discovery for Ni-catalyzed atroposelective Suzuki–Miyaura coupling. A central lesson from these studies is that mechanistic insight is not a replacement for machine learning but the chemical foundation that makes it more reliable, interpretable, and extrapolative. By translating mechanistic understanding into data boundaries, reaction representations, and transfer strategies, machine learning can move beyond retrospective fitting and become a chemically grounded framework for predicting and discovering new organic reactions.