Research note
Angewandte Chemie International Edition
Hypothesis-and-Refinement Learning of Organic Structures From Multimodal Spectroscopic Data
A hypothesis-and-refinement system that proposes molecular structures from multimodal spectra, then repairs them with formula-, exact-mass-, and unsaturation-aware molecular generation.
01 / Problem
Problem
Molecular structure elucidation is an underdetermined inverse problem: each spectrum contains only partial evidence, while many structures can appear plausible.
02 / Approach
Approach
The workflow first proposes candidate structures from complementary spectral evidence, then refines them using explicit molecular-formula, exact-mass, and unsaturation constraints.
03 / Why it matters
Why it matters
Combining multimodal evidence with explicit chemical constraints makes automated structure elucidation more chemically grounded and easier to inspect.
04 / Publication details
Publication details
- Authors
- Chengchun Liu†, Zhiyuan Yan†, Li Yuan, Hao Li, Boxuan Zhao, Yonghong Tian, Bartosz A. Grzybowski, and Fanyang Mo
- Journal
- Angewandte Chemie International Edition
- Year
BibTeX
@article{liu2026spectromol,
title = {Hypothesis-and-Refinement Learning of Organic Structures From Multimodal Spectroscopic Data},
author = {Chengchun Liu and Zhiyuan Yan and Li Yuan and Hao Li and Boxuan Zhao and Yonghong Tian and Bartosz A. Grzybowski and Fanyang Mo},
journal = {Angewandte Chemie International Edition},
year = {2026},
date = {2026-09-11},
eid = {e9212238},
doi = {10.1002/anie.9212238},
url = {https://doi.org/10.1002/anie.9212238}
}