| --- |
| 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]](#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 |
