Position: Machine Learning Models for Reaction Transition States Deserve Better

Understanding reaction mechanisms is central to advances in chemistry, materials science, and drug discovery, where transition states govern reactivity and kinetics. This position paper argues that current ML approaches for transition state (TS) prediction are fundamentally limited by inadequate datasets, weakly grounded methods, and flawed evaluation protocols, leading to overstated progress and questionable chemical reliability. While recent advances have improved performance, these gains are largely superficial when assessed against the core requirements of a valid TS. First, widely used datasets suffer from issues of chemical implausibility, lack of diversity, and insufficient validation, introducing noise and bias into model training. Second, existing methods often neglect essential physical constraints and rely on assumptions like fixed atom mappings, resulting in brittle, non-generalizable models. Third, standard evaluation metrics fail to capture chemical validity, often rewarding plausible but physically incorrect predictions. Empirical analyses, including quantum chemical validation and robustness studies, demonstrate that many reported transition states do not correspond to intended reactions and that models exhibit poor generalization and strong bias toward equilibrium configurations. We contend that meaningful progress in TS prediction requires a principled rethinking of dataset construction, incorporation of physical constraints into model design, and the development of chemically faithful evaluation frameworks.