Datasets:
license: unknown
task_categories:
- tabular-classification
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
pretty_name: MoleculeNet MUV
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: muv.csv
MoleculeNet MUV
Load and return the MUV (Maximum Unbiased Validation) dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through scikit-fingerprints library.
The task is to predict 17 targets designed for validation of virtual screening techniques, based on PubChem BioAssays. 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 | 17 |
| Task type | multitask classification |
| Total samples | 93087 |
| Recommended split | scaffold |
| Recommended metric | AUPRC, AUROC |
References
[1] Ramsundar, Bharath, et al. "Massively multitask networks for drug discovery" arXiv:1502.02072 (2015) https://arxiv.org/abs/1502.02072
[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