Datasets:

Aurigene-AI
/

MoleculeNet_Tox21

license: unknown
task_categories:
  - tabular-classification
  - graph-ml
  - text-classification
tags:
  - chemistry
  - biology
  - medical
  - toxicity
  - admet
  - drug-discovery
  - aurigene
pretty_name: MoleculeNet Tox21
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: tox21.csv

Mirrored by Aurigene AI

Discovery stage: Lead optimization

Nuclear-receptor and stress-response toxicity assays from the Tox21 challenge. Multi-task classification.

Rows: 7,831 (tox21.csv 7,831)

Pairs with Aurigene-AI/ChemBERTa-77M-MTR from our model catalogue.

Upstream: scikit-fingerprints/MoleculeNet_Tox21 - 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 - Protein Target Explorer - Drug Discovery Model Hub

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

Warning: in newer RDKit vesions, 8 molecules from the original dataset are not read correctly due to disallowed hypervalent states of their aluminium atoms (see release notes). This version of the Tox21 dataset contains manual fixes for those molecules, removing additional hydrogens, e.g. [AlH3] -> [Al]. In OGB scaffold split, used for benchmarking, only the first 1 of those problematic 8 is from the test set. Applied mapping is:

"NC(=O)NC1N=C(O[AlH3](O)O)NC1=O" -> "NC(=O)NC1N=C(O[Al](O)O)NC1=O"
"O=CO[AlH3](OC=O)OC=O" -> "O=CO[Al](OC=O)OC=O"
"CC(=O)O[AlH3](O)O" -> "CC(=O)O[Al](O)O"
"CC(=O)O[AlH3](O)OC(C)=O" -> "CC(=O)O[Al](O)OC(C)=O"
"CCOC(=O)/C=C(/C)O[AlH3](OC(C)CC)OC(C)CC" -> "CCOC(=O)/C=C(/C)O[Al](OC(C)CC)OC(C)CC"
"CCCCO[AlH3](OCCCC)OCCCC" -> "CCCCO[Al](OCCCC)OCCCC"
"O=S(=O)(OC[C@H]1O[C@H](O[C@]2(COS(=O)(=O)O[AlH3](O)O)O[C@H](COS(=O)(=O)O[AlH3](O)O)[C@@H](OS(=O)(=O)O[AlH3](O)O)[C@@H]2OS(=O)(=O)O[AlH3](O)O)[C@H](OS(=O)(=O)O[AlH3](O)O)[C@@H](OS(=O)(=O)O[AlH3](O)O)[C@@H]1OS(=O)(=O)O[AlH3](O)O)O[AlH3](O)O.O[AlH3](O)[AlH3](O)O.O[AlH3](O)[AlH3](O)O.O[AlH3](O)[AlH3](O)O.O[AlH3](O)[AlH3](O)O" -> "O=S(=O)(OC[C@H]1O[C@H](O[C@]2(COS(=O)(=O)O[Al](O)O)O[C@H](COS(=O)(=O)O[Al](O)O)[C@@H](OS(=O)(=O)O[Al](O)O)[C@@H]2OS(=O)(=O)O[Al](O)O)[C@H](OS(=O)(=O)O[Al](O)O)[C@@H](OS(=O)(=O)O[Al](O)O)[C@@H]1OS(=O)(=O)O[Al](O)O)O[Al](O)O.O[Al](O)[Al](O)O.O[Al](O)[Al](O)O.O[Al](O)[Al](O)O.O[Al](O)[Al](O)O"
"CCCCCCCCCCCCCCCCCC(=O)O[AlH3](O)O" -> "CCCCCCCCCCCCCCCCCC(=O)O[Al](O)O"

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