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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: clean_smiles |
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dtype: string |
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- name: id |
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dtype: string |
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- name: value |
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dtype: float64 |
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- name: scaffold |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1412399638 |
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num_examples: 12378792 |
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- name: validation |
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num_bytes: 175994570 |
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num_examples: 1547349 |
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- name: test |
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num_bytes: 171911605 |
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num_examples: 1547350 |
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download_size: 739740891 |
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dataset_size: 1760305813 |
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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: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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language: |
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- en |
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tags: |
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- chemistry |
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- biology |
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- virtual-screening |
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- docking |
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- drug-discovery |
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- machine-learning |
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- structure-based-design |
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pretty_name: RO4 Virtual Screening – d2 Target |
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size_categories: |
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- 10M<n<100M |
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description: > |
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This dataset contains docking scores for over 15 million compounds from the Enamine REAL library |
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screened against the d2 protein target using structure-based virtual screening. The data was |
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originally published by Luttens et al. (2022), and includes sanitized SMILES strings, compound |
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identifiers, docking scores, and Bemis-Murcko scaffolds. The dataset is split using scaffold-based |
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splitting to support robust machine learning benchmarking. |
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title: RO4 Virtual Screening Dataset for d2 Target |
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authors: |
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- Luttens et al. |
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date: 2025-03-13T00:00:00.000Z |
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doi: 10.1038/s43588-025-00777-x |
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--- |
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