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

# MoleculeNet ToxCast

ToxCast 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 617 toxicity targets from a large library of compounds based on in vitro high-throughput screening. All tasks are binary.

Note that targets have missing values. Algorithms should be evaluated only on present labels. For training data, you may want to impute them, e.g. with zeros.

| **Characteristic** |      **Description**     |
|:------------------:|:------------------------:|
|        Tasks       |            617           |
|      Task type     | multitask classification |
|    Total samples   |           8576           |
|  Recommended split |         scaffold         |
| Recommended metric |           AUROC          |

## References
<a id="1">[1]</a> 
Ann M. Richard et al.
"ToxCast Chemical Landscape: Paving the Road to 21st Century Toxicology"
Chem. Res. Toxicol. 2016, 29, 8, 1225–1251
https://pubs.acs.org/doi/10.1021/acs.chemrestox.6b00135>

<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