|
|
--- |
|
|
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 |
