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Datasets:
scikit-fingerprints
/
MoleculeNet_Tox21

Tasks:
Tabular Classification
Graph Machine Learning
Text Classification
Modalities:
Tabular
Text
Formats:
csv
Size:
1K - 10K
Tags:
chemistry
biology
medical
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metadata
license: unknown
task_categories:
  - tabular-classification
  - graph-ml
  - text-classification
tags:
  - chemistry
  - biology
  - medical
pretty_name: MoleculeNet Tox21
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: tox21.csv

MoleculeNet Tox21

Tox21 dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through scikit-fingerprints library.

The task is to predict 12 toxicity targets, including nuclear receptors and stress response pathways. 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 12
Task type multitask classification
Total samples 7831
Recommended split scaffold
Recommended metric AUROC

References

[1] Tox21 Challenge https://tripod.nih.gov/tox21/challenge/

[2] 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