Flow matching for reaction pathway generation Tuo, Ping Chen, Jiale ; https://orcid.org/0000-0001-5337-5875 Li, Ju ddc:000 Elucidating reaction mechanisms requires efficient generation of transition states (TSs) and products. Existing diffusion and sequence-based models accelerate parts of this process over traditional string-based methods, but typically still require manual enumeration of either TSs or products, and stochastic diffusion dynamics can be inefficient and hard to control. We introduce MolGEN, a conditional flow-matching framework that uses deterministic optimal transport to map Gaussian priors to chemical distributions. For TS generation, MolGEN improves TS geometry and barrier-height prediction over diffusion models while enabling sub-second sampling. For reaction product generation, it achieves competitive top-k accuracy while preserving mass and electron balance. Using the same backbone for TS and product sampling, MolGEN enables template-free generative exploration of reaction networks without the repeated quantum-chemistry searches required by prior methods. For the γ-ketohydroperoxide decomposition network, it produces more valid TSs than string-based methods using only 12 quantum-chemistry evaluations instead of 1156, and identifies a lower-barrier pathway. Springer Nature 2026 info:eu-repo/semantics/article doc-type:Article Article http://purl.org/coar/resource_type/c_2df8fbb1 https://research-explorer.ista.ac.at/record/22771 https://research-explorer.ista.ac.at/download/22771/22864 Tuo P, Chen J, Li J. Flow matching for reaction pathway generation. <i>Nature Communications</i>. 2026;17. doi:<a href="https://doi.org/10.1038/s41467-026-75654-w">10.1038/s41467-026-75654-w</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-026-75654-w info:eu-repo/semantics/altIdentifier/e-issn/2041-1723 info:eu-repo/semantics/altIdentifier/arxiv/2507.10530 info:eu-repo/semantics/altIdentifier/pmid/42469237 https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess