| ---
|
| license: unknown
|
| task_categories:
|
| - tabular-classification
|
| - graph-ml
|
| - text-classification
|
| tags:
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| - chemistry
|
| - biology
|
| - medical
|
| - toxicity
|
| - admet
|
| - drug-discovery
|
| - aurigene
|
| pretty_name: MoleculeNet SIDER
|
| size_categories:
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| - 1K<n<10K
|
| configs:
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| - config_name: default
|
| data_files:
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| - split: train
|
| path: sider.csv
|
| ---
|
|
|
| <!-- aurigene-header -->
|
| > ### Mirrored by [Aurigene AI](https://huggingface.co/Aurigene-AI)
|
| > **Discovery stage:** Lead optimization
|
| >
|
| > Marketed-drug side effects grouped into MedDRA system organ classes. Multi-task classification.
|
| >
|
| > **Rows:** 1,427 (sider.csv 1,427)
|
| >
|
| > Pairs with [`Aurigene-AI/ChemBERTa-77M-MTR`](https://huggingface.co/Aurigene-AI/ChemBERTa-77M-MTR) from our model catalogue.
|
| >
|
| > Upstream: [`scikit-fingerprints/MoleculeNet_SIDER`](https://huggingface.co/datasets/scikit-fingerprints/MoleculeNet_SIDER) - all credit to the original authors and to the researchers who produced the underlying data; the dataset card and licence below are theirs.
|
| >
|
| > Explore the rest of the catalogue: [Molecule Explorer](https://huggingface.co/spaces/Aurigene-AI/molecule-explorer) - [Protein Target Explorer](https://huggingface.co/spaces/Aurigene-AI/protein-target-explorer) - [Drug Discovery Model Hub](https://huggingface.co/spaces/Aurigene-AI/drug-discovery-model-hub)
|
|
|
| ---
|
|
|
| # MoleculeNet SIDER
|
|
|
| Load and return the SIDER (Side Effect Resource) dataset [[1]](#1), part of MoleculeNet [[2]](#2) benchmark. It is intended to be used through
|
| [scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library.
|
|
|
| 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.
|
|
|
| | **Characteristic** | **Description** |
|
| |:------------------:|:------------------------:|
|
| | Tasks | 12 |
|
| | Task type | multitask classification |
|
| | Total samples | 7831 |
|
| | Recommended split | scaffold |
|
| | Recommended metric | AUROC |
|
|
|
| ## References
|
| <a id="1">[1]</a>
|
| Han Altae-Tran et al.
|
| "Low Data Drug Discovery with One-Shot Learning"
|
| ACS Cent. Sci. 2017, 3, 4, 283–293
|
| https://pubs.acs.org/doi/10.1021/acscentsci.6b00367
|
|
|
| <a id="2">[2]</a>
|
| Wu, Zhenqin, et al.
|
| "MoleculeNet: a benchmark for molecular machine learning."
|
| Chemical Science 9.2 (2018): 513-530
|
| https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a |
