遇见数据集

MoleHB

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魔搭社区2026-05-20 更新2026-05-24 收录
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# MoleHB: Molecular Handbook Benchmark **MoleHB** (Molecular Handbook) is a comprehensive molecular property prediction benchmark introduced in the [Suiren-1.0 Technical Report](https://arxiv.org/abs/2603.21942). It encompasses **40+ heterogeneous tasks** spanning **8 critical scientific domains**, derived from high-fidelity experimental data to facilitate rigorous evaluation of molecular foundation models. ## Overview MoleHB addresses the critical need for standardized, experimentally validated benchmarks in molecular machine learning. All data points are inferred from canonical chemical handbooks (Yaws, 1999) and rigorously validated via wet-lab experiments to ensure high-fidelity, stable values. The benchmark is designed to test both interpolation and extrapolation capabilities of molecular representations. ### Key Features - **Comprehensive Coverage**: 43 molecular properties across safety, structural, thermodynamic, energetic, transport, and solution domains - **Dual Evaluation Protocols**: - **Random Split**: Standard 8:2 random split for in-distribution evaluation - **Scaffold Split**: Size-based scaffold split (heavier molecules in validation) to test structural extrapolation - **Standardized Format**: Simple CSV format with SMILES strings and property values for easy integration ## Dataset Statistics | Domain | # Properties | Description | |--------|-------------|-------------| | Critical & Saturation | 5 | Critical temperature, pressure, density, volume, compressibility | | Safety | 3 | Flash point, explosive limits | | Fluctuation | 4 | Heat capacities (gas/liquid/solid), thermal expansion | | Solution | 6 | Solubility, partition coefficients, Henry's law constants | | Thermal | 3 | Melting point, boiling point, vapor pressure | | Structural | 5 | Density, acentric factor, refractive index, radius of gyration, dipole moment | | Energetic | 9 | Enthalpies, entropies, Gibbs/Helmholtz energies of formation | | Transport | 5 | Viscosity, thermal conductivity, diffusion coefficients | | Others | - | Surface tension, hydration free energy, and etc. | **Total**: 43 regression tasks covering diverse chemical spaces and property ranges. ## Directory Structure ```text MoleHB/ ├── random/ # Random split (80/20) │ ├── critical_temperature/ │ │ └── raw/ │ │ ├── critical_temperature_train.csv # Training set: SMILES,value │ │ └── critical_temperature_valid.csv # Validation set: SMILES,value │ ├── critical_pressure/ │ ├── flash_point/ │ ├── solubility_in_water/ │ └── ... (40+ property directories) │ └── scaffold/ # Scaffold split (structural extrapolation) ├── critical_temperature/ │ └── raw/ │ ├── critical_temperature_train.csv │ └── critical_temperature_valid.csv ├── critical_pressure/ └── ... (4o+ property directories) ``` ## File Format Each CSV file contains two columns: - **SMILES**: Molecular structure in SMILES notation (string) - **value**: Property value in specified units (float) Example: ```csv SMILES,value CCO,78.37 CC(C)O,82.4 c1ccccc1,80.09 ``` ## Property List & Units | Property Name | Unit | Category | |--------------|------|----------| | `critical_temperature` | K | Critical & Saturation | | `critical_pressure` | bar | Critical & Saturation | | `critical_density` | g/ml | Critical & Saturation | | `critical_volume` | cm³/mol | Critical & Saturation | | `critical_compressibility` | cm³ | Critical & Saturation | | `flash_point` | F | Safety | | `lower_explosive_limit` | vol% | Safety | | `upper_explosive_limit` | vol% | Safety | | `heat_capacity_of_liquid` | J/mol/K | Fluctuation | | `heat_capacity_of_solid` | J/mol/K | Fluctuation | | `heat_capacity_of_gas` | J/mol/K | Fluctuation | | `coefficient_of_thermal_expansion_of_liquid` | 1/K | Fluctuation | | `melting_point` | K | Thermal | | `boiling_point` | K | Thermal | | `vapor_pressure` | mmHg | Thermal | | `density_of_liquid` | g/ml | Structural | | `acentric_factor` | omega | Structural | | `refractive_index` | - | Structural | | `radius_of_gyration` | 10⁻¹⁰m | Structural | | `dipole_moment` | D | Structural | | `enthalpy_of_formation` | kJ/mol | Energetic | | `entropy_of_formation` | J/mol/K | Energetic | | `gibbs_energy_of_formation` | J/mol | Energetic | | `helmholtz_energy_of_formation` | kJ/mol | Energetic | | `internal_energy_of_formation` | kJ/mol | Energetic | | `enthalpy_of_vaporization` | kJ/mol | Energetic | | `enthalpy_of_fusion` | J/mol/K | Energetic | | `enthalpy_of_combustion` | kJ/mol | Energetic | | `entropy_of_gas` | J/mol/K | Energetic | | `solubility_in_water` | ppm(wt) | Solution | | `solubility_in_water_containing_salt` | ppm(wt) | Solution | | `octanol_water_partition_coefficient` | - | Solution | | `solubility_parameter` | (J/cm³)^1/2 | Solution | | `henrys_law_constant_for_compound_in_water` | - | Solution | | `henrys_law_constant_for_gas_in_water` | - | Solution | | `viscosity_of_liquid` | mp | Transport | | `thermal_conductivity_of_gas` | W/m/K | Transport | | `thermal_conductivity_of_liquid` | W/m/K | Transport | | `diffusion_coefficient_at_infinite_dilution_in_water` | cm²/s | Transport | | `diffusion_coefficient_in_air` | cm²/s | Transport | | `surface_tension` | dynes/cm | Other | | `hydration_free_energy` | kJ/mol | Other | ## Evaluation Metrics - **Primary Metric**: Mean Absolute Error (MAE) for regression tasks - **Secondary Metric**: Coefficient of Determination (R²) ## Citation If you use MoleHB in your research, please cite: ```bibtex @article{an2026suiren, title={Suiren-1.0 Technical Report: A Family of Molecular Foundation Models}, author={An, Junyi and Lu, Xinyu and Shi, Yun-Fei and Xu, Li-Cheng and Zhang, Nannan and Qu, Chao and Qi, Yuan and Cao, Fenglei}, journal={arXiv preprint arXiv:2603.21942}, year={2026} } ``` ## License This dataset is released under the Apache-2.0 License for academic and commercial use. --- **Note**: This benchmark is part of the Suiren-1.0 molecular foundation model family. For pre-trained model weights and fine-tuning scripts, please refer to the main Suiren-1.0 repository. ```

提供机构:
maas
创建时间:
2026-03-23
搜集汇总
数据集介绍
MoleHB 数据集图片
背景与挑战
背景概述
MoleHB是一个用于分子性质预测的综合性基准,包含超过40个异质任务,覆盖8个关键科学领域,数据来源于高保真实验数据。该数据集提供随机和骨架两种分割方式,采用CSV格式存储SMILES字符串和属性值,旨在严格评估分子基础模型的插值和外推能力。
以上内容由遇见数据集搜集并总结生成
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