Drift-React proposes an SE(3)-equivariant generative model that outputs full minimum energy pathways (MEPs) directly from reactant and product geometries in one forward pass, sidestepping both the thousands of force calls in nudged elastic band (NEB) methods and the sequential ODE/SDE sampling of diffusion or flow-matching approaches. Trained via a Sinkhorn-weighted drifting field that internalizes distribution evolution, the framework was tested on the Transition1x dataset (roughly 10k organic reactions with DFT-computed paths) and the smaller Halo8 set of halogenation reactions. While the preprint claims sub-Ångström transition-state accuracy matching iterative baselines at orders-of-magnitude speed-up, it remains a non-peer-reviewed arXiv submission from May 2026 and lacks external experimental validation or tests on larger biomolecular systems. Prior work such as the 2023 Transition1x benchmark paper (J. Chem. Phys.) established the data scale needed for learning reaction coordinates, while flow-matching papers like those extending to SE(3) manifolds (e.g., 2024 NeurIPS contributions on equivariant flows) highlighted inference cost as the remaining bottleneck; Drift-React directly addresses the latter but under-explores whether single-pass outputs preserve detailed-balance consistency across multi-step catalytic cycles. This matters for drug and materials pipelines where exhaustive reaction-network enumeration remains prohibitive; if the drifting-field formulation generalizes, it could compress weeks-long DFT campaigns into hours, yet the absence of ablation on equivariance violations or solvent effects leaves open questions about robustness in realistic conditions.