--- license: apache-2.0 language: - en configs: - config_name: MolCustom_AtomNum data_files: - split: test path: "MolCustom_AtomNum.csv" - config_name: MolCustom_BondNum data_files: - split: test path: "MolCustom_BondNum.csv" - config_name: MolCustom_FunctionalGroup data_files: - split: test path: "MolCustom_FunctionalGroup.csv" - config_name: MolEdit_AddComponent data_files: - split: test path: "MolEdit_AddComponent.csv" - config_name: MolEdit_SubComponent data_files: - split: test path: "MolEdit_SubComponent.csv" - config_name: MolEdit_DelComponent data_files: - split: test path: "MolEdit_DelComponent.csv" - config_name: MolOpt_LogP data_files: - split: test path: "MolOpt_LogP.csv" - config_name: MolOpt_MR data_files: - split: test path: "MolOpt_MR.csv" - config_name: MolOpt_QED data_files: - split: test path: "MolOpt_QED.csv" --- # S^2-Bench Dataset (TOMG) (full version, 45k entries) Official Huggingface Datasets for [S^2-Bench](https://phenixace.github.io/tomgbench/): *"Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation"* Please refer to our [Github Repo](https://github.com/phenixace/S2-TOMG-Bench) 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 ```python 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: ```bibtex @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 | Rank | Model | \#Parameters (B) | $\overline{S\!R}$ (\%) | $\overline{W\!S\!R} (\%)$ | | |------|-----------------------------------------------------|------------------|------------------------|---------------------------|---| | 1 | Llama3.1-8B (OpenMolIns-xlarge) | 8 | 58.79 | 39.33 | | | 2 | Claude-3.5 | - | 51.10 | 35.92 | | | 3 | Gemini-1.5-pro | - | 52.25 | 34.80 | | | 4 | GPT-4-turbo | - | 50.74 | 34.23 | | | 5 | GPT-4o | - | 49.08 | 32.29 | | | 6 | Claude-3 | - | 46.14 | 30.47 | | | 7 | Llama3.1-8B (OpenMolIns-large) | 8 | 43.1 | 27.22 | | | 8 | Galactica-125M (OpenMolIns-xlarge) | 0.125 | 44.48 | 25.73 | | | 9 | Llama3-70B-Instruct (Int4) | 70 | 38.54 | 23.93 | | | 10 | Galactica-125M (OpenMolIns-large) | 0.125 | 39.28 | 23.42 | | | 11 | Galactica-125M (OpenMolIns-medium) | 0.125 | 34.54 | 19.89 | | | 12 | GPT-3.5-turbo | - | 28.93 | 18.58 | | | 13 | Galactica-125M (OpenMolIns-small) | 0.125 | 24.17 | 15.18 | | | 14 | Gemma3-12B | 12 | 26.28 | 15.00 | | | 15 | Deepseek-R1-distill-Qwen-7B | 7 | 25.07 | 14.61 | | | 16 | Llama3.1-8B-Instruct | 8 | 26.26 | 14.09 | | | 17 | Llama3-8B-Instruct | 8 | 26.40 | 13.75 | | | 18 | chatglm-9B | 9 | 18.50 | 13.13(7) | | | 19 | Galactica-125M (OpenMolIns-light) | 0.125 | 20.95 | 13.13(6) | | | 20 | ChemDFM-v1.5-8B | 8 | 18.24 | 12.07 | | | 21 | ChemLLM-20B | 20 | 16.23 | 9.76 | | | 22 | Llama3.2-1B (OpenMolIns-large) | 1 | 14.11 | 8.10 | | | 23 | yi-1.5-9B | 9 | 14.10 | 7.32 | | | 24 | Mistral-7B-Instruct-v0.2 | 7 | 11.17 | 4.81 | | | 25 | BioT5-base | 0.25 | 24.19 | 4.21 | | | 26 | MolT5-large | 0.78 | 23.11 | 2.89 | | | 27 | Llama3.1-1B-Instruct | 1 | 3.95 | 1.99 | | | 28 | MolT5-base | 0.25 | 11.11 | 1.30(0) | | | 29 | MolT5-small | 0.08 | 11.55 | 1.29(9) | | | 30 | Qwen2-7B-Instruct | 7 | 0.18 | 0.15 | | ## OpenMolIns The instruction tuning datasets are also available at Hugging Face Datasets: 1. [OpenMolIns-light](https://huggingface.co/datasets/phenixace/OpenMolIns-light) 2. [OpenMolIns-small](https://huggingface.co/datasets/phenixace/OpenMolIns-small) 3. [OpenMolIns-medium](https://huggingface.co/datasets/phenixace/OpenMolIns-medium) 4. [OpenMolIns-large](https://huggingface.co/datasets/phenixace/OpenMolIns-large) 5. [OpenMolIns-xlarge](https://huggingface.co/datasets/phenixace/OpenMolIns-xlarge)