A unified framework for automated transition state generation to accelerate mechanistic exploration in organic synthesis

Transition states (TS) are pivotal for elucidating reaction mechanisms, but their calculation constitutes a major bottleneck. While generative AI provides a route to automate transition state generation, existing methods remain confined to simplified chemical systems, lacking generalizability to complex reactions like transition metal catalysis. Here, we present a unified framework for general-purpose TS discovery, comprising the UniTS-Lib library of 4,391 high-fidelity structures spanning 42 elements and diverse chemical transformations, coupled with the UniTS-Gen diffusion model that generates 3D TS configurations from 2D reactant graphs using a higher-degree equivariant network. Validation demonstrates UniTS-Gen's superior structural accuracy and robust generalization to unseen chemical systems. We show that UniTS-Gen provides highly reliable initial guesses and actively accelerates discovery by locating kinetically preferred TS conformation. This work provides a scalable and transferable solution for automating mechanistic studies in organic synthesis.