---
license: unknown
task_categories:
- tabular-classification
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
pretty_name: MoleculeNet SIDER
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: "sider.csv"
---

# 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