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--- |
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license: unknown |
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task_categories: |
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- tabular-classification |
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- graph-ml |
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- text-classification |
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tags: |
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- chemistry |
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- biology |
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- medical |
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pretty_name: MoleculeNet SIDER |
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size_categories: |
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- 1K<n<10K |
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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: "sider.csv" |
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--- |
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# MoleculeNet SIDER |
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Load and return the SIDER (Side Effect Resource) dataset [[1]](#1), part of MoleculeNet [[2]](#2) benchmark. It is intended to be used through |
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[scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library. |
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The task is to predict adverse drug reactions (ADRs) as drug side effects to 27 system organ classes in MedDRA classification. All tasks are binary. |
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| **Characteristic** | **Description** | |
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|:------------------:|:------------------------:| |
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| Tasks | 12 | |
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| Task type | multitask classification | |
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| Total samples | 7831 | |
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| Recommended split | scaffold | |
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| Recommended metric | AUROC | |
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## References |
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<a id="1">[1]</a> |
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Han Altae-Tran et al. |
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"Low Data Drug Discovery with One-Shot Learning" |
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ACS Cent. Sci. 2017, 3, 4, 283–293 |
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https://pubs.acs.org/doi/10.1021/acscentsci.6b00367 |
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<a id="2">[2]</a> |
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Wu, Zhenqin, et al. |
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"MoleculeNet: a benchmark for molecular machine learning." |
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Chemical Science 9.2 (2018): 513-530 |
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https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a |
