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license: unknown
task_categories:
- tabular-classification
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
- toxicity
- admet
- drug-discovery
- aurigene
pretty_name: MoleculeNet SIDER
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- 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 |