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Datasets:
scikit-fingerprints
/
MoleculeNet_SIDER

Tasks:
Tabular Classification
Graph Machine Learning
Text Classification
Modalities:
Tabular
Text
Formats:
csv
Size:
1K - 10K
Tags:
chemistry
biology
medical
Libraries:
Datasets
pandas
License:
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metadata
license: unknown
task_categories:
  - tabular-classification
  - graph-ml
  - text-classification
tags:
  - chemistry
  - biology
  - medical
pretty_name: MoleculeNet SIDER
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: sider.csv

MoleculeNet SIDER

Load and return the SIDER (Side Effect Resource) dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through scikit-fingerprints library.

The task is to predict adverse drug reactions (ADRs) as drug side effects to 27 system organ classes in MedDRA classification. All tasks are binary.

Characteristic Description
Tasks 12
Task type multitask classification
Total samples 7831
Recommended split scaffold
Recommended metric AUROC

References

[1] Han Altae-Tran et al. "Low Data Drug Discovery with One-Shot Learning" ACS Cent. Sci. 2017, 3, 4, 283–293 https://pubs.acs.org/doi/10.1021/acscentsci.6b00367

[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