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--- |
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license: unknown |
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task_categories: |
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- tabular-classification |
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- graph-ml |
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- text-classification |
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tags: |
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- chemistry |
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- biology |
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- medical |
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pretty_name: MoleculeNet MUV |
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size_categories: |
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- 10K<n<100K |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: "muv.csv" |
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--- |
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# MoleculeNet MUV |
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Load and return the MUV (Maximum Unbiased Validation) dataset [[1]](#1), part of MoleculeNet [[2]](#2) benchmark. It is intended to be used through |
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[scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library. |
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The task is to predict 17 targets designed for validation of virtual screening techniques, based on PubChem BioAssays. All tasks are binary. |
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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. |
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| **Characteristic** | **Description** | |
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|:------------------:|:------------------------:| |
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| Tasks | 17 | |
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| Task type | multitask classification | |
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| Total samples | 93087 | |
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| Recommended split | scaffold | |
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| Recommended metric | AUPRC, AUROC | |
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## References |
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<a id="1">[1]</a> |
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Ramsundar, Bharath, et al. |
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"Massively multitask networks for drug discovery" |
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arXiv:1502.02072 (2015) |
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https://arxiv.org/abs/1502.02072 |
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<a id="2">[2]</a> |
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Wu, Zhenqin, et al. |
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"MoleculeNet: a benchmark for molecular machine learning." |
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Chemical Science 9.2 (2018): 513-530 |
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https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a |
