--- 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 > ### 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 [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