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license: unknown |
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# Dataset Card for ZINC |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [External Use](#external-use) |
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- [PyGeometric](#pygeometric) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Properties](#data-properties) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Additional Information](#additional-information) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **[Homepage](https://zinc15.docking.org/)** |
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- **[Repository](https://www.dropbox.com/s/feo9qle74kg48gy/molecules.zip?dl=1):**: |
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- **Paper:**: ZINC 15 – Ligand Discovery for Everyone (see citation) |
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- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/) |
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### Dataset Summary |
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The `ZINC` dataset is a "curated collection of commercially available chemical compounds prepared especially for virtual screening" (Wikipedia). |
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### Supported Tasks and Leaderboards |
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`ZINC` should be used for molecular property prediction (aiming to predict the constrained solubility of the molecules), a graph regression task. The score used is the MAE. |
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The associated leaderboard is here: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-regression-on-zinc). |
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## External Use |
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### PyGeometric |
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To load in PyGeometric, do the following: |
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```python |
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from datasets import load_dataset |
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from torch_geometric.data import Data |
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from torch_geometric.loader import DataLoader |
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dataset_hf = load_dataset("graphs-datasets/<mydataset>") |
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# For the train set (replace by valid or test as needed) |
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dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]] |
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dataset_pg = DataLoader(dataset_pg_list) |
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``` |
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## Dataset Structure |
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### Data Properties |
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| property | value | |
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|---|---| |
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| scale | big | |
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| #graphs | 220011 | |
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| average #nodes | 23.15 | |
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| average #edges | 49.81 | |
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### Data Fields |
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Each row of a given file is a graph, with: |
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- `node_feat` (list: #nodes x #node-features): nodes |
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- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges |
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- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features |
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- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one) |
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- `num_nodes` (int): number of nodes of the graph |
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### Data Splits |
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This data comes from the PyGeometric version of the dataset, and follows the provided data splits. |
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This information can be found back using |
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```python |
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from torch_geometric.datasets import ZINC |
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dataset = ZINC(root = '', split='train') # valid, test |
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``` |
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## Additional Information |
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### Licensing Information |
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The dataset has been released under unknown license. Please open an issue if you know what is the license of this dataset. |
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### Citation Information |
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```bibtex |
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@article{doi:10.1021/acs.jcim.5b00559, |
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author = {Sterling, Teague and Irwin, John J.}, |
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title = {ZINC 15 – Ligand Discovery for Everyone}, |
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journal = {Journal of Chemical Information and Modeling}, |
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volume = {55}, |
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number = {11}, |
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pages = {2324-2337}, |
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year = {2015}, |
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doi = {10.1021/acs.jcim.5b00559}, |
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note ={PMID: 26479676}, |
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URL = { |
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https://doi.org/10.1021/acs.jcim.5b00559 |
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}, |
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eprint = { |
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https://doi.org/10.1021/acs.jcim.5b00559 |
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} |
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} |
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``` |
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### Contributions |
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Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. |
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