---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Ping
      foaf_name: Tuo, Ping
      foaf_surname: Tuo
      foaf_workInfoHomepage: http://www.librecat.org/personId=6e5644c0-c180-11ed-a2da-facc4c9f4f09
  - foaf_Person:
      foaf_givenName: Jiale
      foaf_name: Chen, Jiale
      foaf_surname: Chen
      foaf_workInfoHomepage: http://www.librecat.org/personId=4d0a9064-1ff6-11ee-9fa6-ec046c604785
    orcid: 0000-0001-5337-5875
  - foaf_Person:
      foaf_givenName: Ju
      foaf_name: Li, Ju
      foaf_surname: Li
  bibo_doi: 10.1038/s41467-026-75654-w
  bibo_volume: 17
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2041-1723
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: Flow matching for reaction pathway generation@
  fabio_hasPubmedId: '42469237'
...