| ---
|
| 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
|
| ---
|
|
|
| <!-- aurigene-header -->
|
| > ### Mirrored by [Aurigene AI](https://huggingface.co/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`](https://huggingface.co/Aurigene-AI/ChemBERTa-77M-MTR) from our model catalogue.
|
| >
|
| > Upstream: [`scikit-fingerprints/MoleculeNet_Tox21`](https://huggingface.co/datasets/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](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 Tox21
|
|
|
| Tox21 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 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](https://github.com/rdkit/rdkit/releases/tag/Release_2024_09_1)).
|
| 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
|
| <a id="1">[1]</a>
|
| Tox21 Challenge
|
| https://tripod.nih.gov/tox21/challenge/
|
|
|
| <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 |
