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cid
string
split
string
iupac_name
string
smiles
string
brics_gids
list
entropy_gids
list
graph_data
dict
1
pretrain
3-acetyloxy-4-(trimethylazaniumyl)butanoate
CC(=O)OC(CC(=O)[O-])C[N+](C)(C)C
[ 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3 ]
[ 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 3 ]
{ "unimol": { "src_tokens": [ 1, 4, 4, 6, 6, 4, 4, 4, 6, 6, 4, 5, 4, 4, 4, 2 ], "src_edge_type": [ [ 32, 35, 35, 37, 37, 35, 35, 35, ...
1000
pretrain
2-amino-1-phenylethanol
C1=CC=C(C=C1)C(CN)O
[ 0, 0, 0, 0, 0, 0, 1, 1, 1, 1 ]
[ 0, 0, 0, 0, 0, 0, 1, 1, 1, 2 ]
{ "unimol": { "src_tokens": [ 1, 4, 4, 4, 4, 4, 4, 4, 4, 5, 6, 2 ], "src_edge_type": [ [ 32, 35, 35, 35, 35, 35, 35, 35, 35, 36, 37, ...
10000
pretrain
3-chloro-1,1,1-trifluoropropane
C(CCl)C(F)(F)F
[ 0, 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 1, 1, 1, 1 ]
{ "unimol": { "src_tokens": [ 1, 4, 4, 9, 4, 10, 10, 10, 2 ], "src_edge_type": [ [ 32, 35, 35, 40, 35, 41, 41, 41, 33 ], [ 125, 128, 128, ...
10000009
pretrain
N-[(2S,3R,4R,5R,6R)-4,5-dihydroxy-6-(hydroxymethyl)-2-[(2R,3S,4R,5R)-4,5,6-trihydroxy-2-(hydroxymethyl)oxan-3-yl]oxyoxan-3-yl]acetamide
CC(=O)N[C@@H]1[C@H]([C@H]([C@H](O[C@H]1O[C@@H]2[C@H](OC([C@@H]([C@H]2O)O)O)CO)CO)O)O
[ 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 6, 6, 2, 2 ]
[ 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 5, 5 ]
{ "unimol": { "src_tokens": [ 1, 4, 4, 6, 5, 4, 4, 4, 4, 6, 4, 6, 4, 4, 6, 4, 4, 4, 6, 6, 6, 4, 6, 4, 6, 6, 6, 2 ], "src_edge_type": [ ...
10000015
pretrain
N-[(9S)-5,19-dimethoxy-6-oxo-15,17-dioxatetracyclo[10.7.0.02,8.014,18]nonadeca-1(19),2,4,7,12,14(18)-hexaen-9-yl]acetamide
CC(=O)N[C@H]1CCC2=CC3=C(C(=C2C4=CC=C(C(=O)C=C14)OC)OC)OCO3
[ 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 1, 1, 1 ]
[ 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5 ]
{ "unimol": { "src_tokens": [ 1, 4, 4, 6, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 6, 4, 6, 4, 6, 4, 6, 2 ], "src...
10000052
pretrain
(1S,4'R,6S,10R,11S)-11-chloro-4'-hydroxy-3',4,5-trimethoxyspiro[7-azatricyclo[4.3.3.01,6]dodec-4-ene-10,5'-cyclopent-2-ene]-1',3-dione
COC1=CC(=O)[C@@]2([C@H]1O)[C@H](C[C@@]34[C@@]2(CCN3)CC(=O)C(=C4OC)OC)Cl
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2 ]
{ "unimol": { "src_tokens": [ 1, 4, 6, 4, 4, 4, 6, 4, 4, 6, 4, 4, 4, 4, 4, 4, 5, 4, 4, 6, 4, 4, 6, 4, 6, 4, 9, 2 ], "src_edge_type": [ ...
10000195
pretrain
2,6-ditert-butyl-4-(4-methoxyphenyl)pyrylium;tetrafluoroborate
[B-](F)(F)(F)F.CC(C)(C)C1=CC(=CC(=[O+]1)C(C)(C)C)C2=CC=C(C=C2)OC
[ 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 5, 5 ]
[ 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5 ]
{ "unimol": { "src_tokens": [ 1, 15, 10, 10, 10, 10, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 4, 2 ], "src_edg...
100002
pretrain
10,14-dimethyl-5-propan-2-yl-3,6,16-trioxapentacyclo[8.6.1.02,4.04,9.014,17]heptadec-8-ene-7,15-dione
CC(C)C1C23C(O2)C4C5C(C3=CC(=O)O1)(CCCC5(C(=O)O4)C)C
[ 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
[ 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3 ]
{ "unimol": { "src_tokens": [ 1, 4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4, 6, 6, 4, 4, 4, 4, 4, 6, 6, 4, 4, 2 ], "src_edge_type": [ [ 32, ...
100004
pretrain
methyl 18-ethyl-8,14-diazapentacyclo[9.5.2.01,9.02,7.014,17]octadeca-2,4,6,9-tetraene-10-carboxylate
CCC1C2CCN3C1C4(CC3)C5=CC=CC=C5NC4=C2C(=O)OC
[ 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2 ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 ]
{ "unimol": { "src_tokens": [ 1, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 6, 6, 4, 2 ], "src_edge_type": [ [ 32, ...
10000451
pretrain
3,5-dihydroxy-2-(3-hydroxy-4-methoxyphenyl)-6,7,8-trimethoxychromen-4-one
COC1=C(C=C(C=C1)C2=C(C(=O)C3=C(C(=C(C(=C3O2)OC)OC)OC)O)O)O
[ 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 5, 5, 2, 2, 1 ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 3, 3 ]
{ "unimol": { "src_tokens": [ 1, 4, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4, 6, 6, 4, 6, 4, 6, 4, 6, 6, 6, 2 ], "src...
10000456
pretrain
1'-[2-[4-(trifluoromethyl)phenyl]ethyl]spiro[1H-3,1-benzoxazine-4,4'-piperidine]-2-one
C1CN(CCC12C3=CC=CC=C3NC(=O)O2)CCC4=CC=C(C=C4)C(F)(F)F
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3 ]
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3 ]
{ "unimol": { "src_tokens": [ 1, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 10, 10, 10, 2 ], "...
10000469
pretrain
[(1R,2R,4R,9R,11S,13S,14R)-2-hydroxy-5,5,9-trimethyl-17-methylidene-10,16-dioxo-15-oxatetracyclo[11.2.2.01,14.04,9]heptadecan-11-yl] acetate
CC(=O)O[C@H]1C[C@@H]2[C@@H]3[C@](O3)([C@@H](C[C@H]4[C@](C1=O)(CCCC4(C)C)C)O)C(=O)C2=C
[ 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 ]
[ 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3 ]
{ "unimol": { "src_tokens": [ 1, 4, 4, 6, 6, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 4, 4, 4, 6, 4, 6, 4, 4, 2 ], "src...
End of preview.

EDT-Former Encoder Pretraining Data

Pretraining corpus for the EDT-Former Stage 1 encoder, as described in the ICLR 2026 paper:

Entropy-Guided Dynamic Tokens for Graph-LLM Alignment in Molecular Understanding Zihao Jing, Qiuhao Zeng, Ruiyi Fang, Yan Sun, Boyu Wang, Pingzhao Hu ICLR 2026 · Paper · Code

Dataset Description

This dataset is used to train the Stage 1 EDT-Former encoder, which learns to align molecular graph representations with text. It is derived from PubChem molecules annotated with BRICS fragment IDs and entropy-guided graph IDs.

Data Format

Each split is stored as a JSONL file where each line is a JSON object representing one molecule-text pair, including graph features, SMILES, and associated text descriptions.

Split File Size
Train train.jsonl ~12 GB
Validation val.jsonl ~37 MB
Test test.jsonl ~78 MB

Usage

from datasets import load_dataset

dataset = load_dataset("zihaojing/EDT-Former-pretrain-data")

Or point to it in the EDT-Former config:

# configs/stage1_dqw2d/data_config_preprocessed.yaml
use_preprocessed: true
preprocessed_data: zihaojing/EDT-Former-pretrain-data

Related Resources

Resource Link
SFT Data zihaojing/EDT-Former-sft-data
Encoder (Stage 1) zihaojing/EDT-Former-encoder
Full Model (Stage 2) zihaojing/EDT-Former-model
Code selmiss/DQ-Former

Citation

@inproceedings{jing2026edtformer,
  title={Entropy-Guided Dynamic Tokens for Graph-LLM Alignment in Molecular Understanding},
  author={Jing, Zihao and Zeng, Qiuhao and Fang, Ruiyi and Sun, Yan and Wang, Boyu and Hu, Pingzhao},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2026}
}
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