license: cc-by-nc-4.0
dataset_info:
features:
- name: graph_data
struct:
- name: edge_attr
sequence:
sequence: int64
- name: edge_index
sequence:
sequence: int64
- name: pos
sequence:
sequence: float64
- name: solvent_class
dtype: int64
- name: x
sequence:
sequence: float64
- name: c_peaks
sequence:
sequence: float64
- name: h_peaks
sequence:
sequence: float64
- name: filename
dtype: string
splits:
- name: train
num_bytes: 176931469
num_examples: 21869
- name: eval
num_bytes: 3856453
num_examples: 479
- name: eval_zeroshot
num_bytes: 1528168
num_examples: 170
- name: eval_fewshot
num_bytes: 1636520
num_examples: 188
download_size: 44790710
dataset_size: 183952610
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
- split: eval_zeroshot
path: data/eval_zeroshot-*
- split: eval_fewshot
path: data/eval_fewshot-*
2DNMRGym
This dataset contains more than 22,000 2D HSQC spectrum for atom-level graph learning tasks.
To use this data, please cite: [https://arxiv.org/abs/2505.18181]
Dataset Details
Dataset Description
HSQC is one important type of 2D NMR experiments, which is crucial in molecular discrimination and structure elucidation tasks. In 2DNMRGym, the train split is annotated by an established algorithm, which provides silver-standard labels for surrogated training. The evluation split is annotated by human experts with more than 10 years of training in organic chemistry, and provids high-quality gold-standard labels for model testing.
Dataset Sources [optional]
The raw data is sourced from HMDB and CH-NMR-NP.
- Repository: [https://github.com/siriusxiao62/Mol2DNMR]
Dataset Structure
Graph: dictionary presentation of pyg graph data. This is the molecular graph for each spectrum, containing node features and 3D coordinants.
C-peaks: the carbon chemical shift of the cross peaks in each HSQC spectrum, annotated to match the carbon indices in the molecular graph.
H-peaks: the proton chemical shifts of the cross peaks in each HSQC spectrum, annotated using the corresponding carbon indicies.
Who are the annotators?
Dr. Hao Xu - Harvard Medical School.
Dr. Wang Duosheng - Boston College.
Dr. Kumar Ambrish - University of Georgia.