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--- |
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license: apache-2.0 |
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dataset_info: |
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features: |
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- name: Smiles |
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dtype: string |
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- name: DockingScore |
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dtype: float64 |
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- name: dG |
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dtype: float64 |
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- name: dGError |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 641714 |
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num_examples: 8997 |
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- name: test |
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num_bytes: 71163 |
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num_examples: 1000 |
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download_size: 315048 |
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dataset_size: 712877 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: test |
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path: data/test-* |
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tags: |
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- molecule |
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- chemistry |
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- smiles |
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- free_energy |
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size_categories: |
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- 1K<n<10K |
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--- |
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Molecular dataset: 10,000 TYK2 inhibitors (SMILES strings) with Docking scores and Relative Binding Free Energy (dG) |
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Dataset from paper: |
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James Thompson, W Patrick Walters, Jianwen A Feng, Nicolas A Pabon, Hongcheng Xu, Michael Maser, Brian B Goldman, Demetri Moustakas, Molly Schmidt, Forrest York, |
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Optimizing active learning for free energy calculations, Artificial Intelligence in the Life Sciences, Volume 2, 2022, 100050, ISSN 2667-3185, |
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https://doi.org/10.1016/j.ailsci.2022.100050. |
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https://www.sciencedirect.com/science/article/pii/S2667318522000204 |
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original source: https://github.com/google-research/google-research/tree/master/al_for_fep |
