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--- |
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license: openrail |
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pretty_name: PubChem 68K |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- image-to-text |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: smiles |
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dtype: string |
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- name: selfies |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1185846198.576 |
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num_examples: 68996 |
|
|
- name: test |
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|
num_bytes: 267097779.576 |
|
|
num_examples: 15499 |
|
|
- name: validation |
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|
num_bytes: 266912227.912 |
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|
num_examples: 15499 |
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|
download_size: 1692942822 |
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|
dataset_size: 1719856206.064 |
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|
--- |
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Molecules in this set |
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* have a molecular weight of fewer than 1500 Daltons, |
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* not possess counter ions, |
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* only contain the elements C, H, O, N, P, S, F, Cl, Br, I, Se and B, |
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* not contain isotopes of Hydrogens (D, T), |
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* have 3–40 bonds, |
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|
* not contain any charged groups including zwitterionic forms, |
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* only contain implicit hydrogens, except in functional groups, |
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* have less than 40 SMILES characters, |
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* no stereochemistry is allowed. |
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The original dataset from Decimer was imported and randomly sampled. 516x516 sized images were generated using RDKit. |
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## Reference |
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> Rajan, Kohulan; Zielesny, Achim; Steinbeck, Christoph (2021): DECIMER 1.0: Deep Learning for Chemical Image Recognition using Transformers. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.14479287.v1 |
