phenixace/S2-TOMG-Bench

S^2-Bench Dataset (TOMG) (full version, 45k entries)

Official Huggingface Datasets for S^2-Bench: "Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation"

Please refer to our Github Repo for more usage and useful information.

Configurations

Each configuration represents a different task:

• —MolCustom_AtomNum: Molecular customized generation by atom number
• —MolCustom_BondNum: Molecular customized generation by bond number
• —MolCustom_FunctionalGroup: Molecular customized generation by functional group
• —MolEdit_AddComponent: Molecular editing - adding components
• —MolEdit_SubComponent: Molecular editing - substituting components
• —MolEdit_DelComponent: Molecular editing - deleting components
• —MolOpt_LogP: Molecular optimization for LogP
• —MolOpt_MR: Molecular optimization for MR
• —MolOpt_QED: Molecular optimization for QED

Usage

from datasets import load_dataset

# Load a specific configuration
dataset = load_dataset("phenixace/S2-TOMG-Bench", "MolCustom_AtomNum")

# Or load all configurations
configs = ["MolCustom_AtomNum", "MolCustom_BondNum", "MolCustom_FunctionalGroup",
           "MolEdit_AddComponent", "MolEdit_SubComponent", "MolEdit_DelComponent",
           "MolOpt_LogP", "MolOpt_MR", "MolOpt_QED"]

datasets = {config: load_dataset("phenixace/S2-TOMG-Bench", config) for config in configs}

Citation

If you use our data, please cite us in the format below:

@article{li2024speak,
  title={Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation},
  author={Li, Jiatong and Li, Junxian and Liu, Yunqing and Zheng, Changmeng and Wei, Xiaoyong and Zhou, Dongzhan and Li, Qing},
  journal={arXiv preprint arXiv:2412.14642v3},
  year={2024}
}

Current Leaderboard

OpenMolIns

The instruction tuning datasets are also available at Hugging Face Datasets:

1. 1.OpenMolIns-light
2. 2.OpenMolIns-small
3. 3.OpenMolIns-medium
4. 4.OpenMolIns-large
5. 5.OpenMolIns-xlarge