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---
license: mit
language:
- en
tags:
- chemistry
- biology
size_categories:
- 1K<n<10K
---
# Tasks
## mol_und
- fg-level
- `fg_count.json`
- 100 samples across 38 different functional groups detection
- `ring_count.json`
- 20 samples, 9 types of ring unit
- scaffold-level
- `Murcko_scaffold.json`
- 40 samples, using MurckoScaffold extraction
- `ring_system_scaffold.json`
- 60 samples, extract ring system as scaffolds
- SMILES-level
- `equivalence.json`
- 50 samples, each smiles -> mutate -> permutate, mutated smiles differs from original smiles
- 50 samples, each smiles -> permutate, permutated smiles is same with original smiles
## mol_edit
- `add.json`
- 20 samples, covers 10 func groups addition
- `delete.json`
- 20 samples, covers 10 func groups deletion
- `sub.json`
- 60 samples, covers 37 func groups substitution
## mol_opt
- `drd.json`
- 100 samples, target-level
- `gsk.json`
- 100 samples, target-level
- `jnk.json`
- 100 samples, target-level
- `logp.json`
- 100 samples, physicochemical-level
- `qed.json`
- 100 samples, physicochemical-level
- `solubility.json`
- 100 samples, physicochemical-level
## reaction
- forward reaction prediction
- `fs.json`
- 100 samples, 100 rxn-cls (each has one sample)
- (single-step) retrosynthesis prediction
- `retro.json`
- 100 samples, 100 rxn-cls (each has one sample)
- reaction condition prediction/recommendation
- `rcr.json`
- 90 samples. 10 types of reaction. Each type has 3 samples for 'Catalyst' prediction, 3 for 'Reagent' prediction and 3 for 'Solvent' prediction
- Next elementary step product prediction (NEPP)
- `nepp.json`
- given former elementary steps description, predict next elementary step's product.
- 85 rxn cls, each has 1 sample
- Mechanism Route Selection (MechSel)
- `mechsel.json`
- 100 samples
# Links
- Our larger CoT dataset [ChemCoTBench-CoT](https://huggingface.co/datasets/OpenMol/ChemCoTBench-CoT)
# Citation
If you find our work helpful, feel free to give us a cite.
```
@article{li2025chemicalqaevaluatingllms,
title={Beyond Chemical QA: Evaluating LLM's Chemical Reasoning with Modular Chemical Operations},
author={Hao Li and He Cao and Bin Feng and Yanjun Shao and Xiangru Tang and Zhiyuan Yan and Li Yuan and Yonghong Tian and Yu Li},
year={2025},
eprint={2505.21318},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2505.21318},
}
```