Datasets:

Aurigene-AI
/

MoleculeNet_SIDER

license: unknown
task_categories:
  - tabular-classification
  - graph-ml
  - text-classification
tags:
  - chemistry
  - biology
  - medical
  - toxicity
  - admet
  - drug-discovery
  - aurigene
pretty_name: MoleculeNet SIDER
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: sider.csv

Mirrored by Aurigene AI

Discovery stage: Lead optimization

Marketed-drug side effects grouped into MedDRA system organ classes. Multi-task classification.

Rows: 1,427 (sider.csv 1,427)

Pairs with Aurigene-AI/ChemBERTa-77M-MTR from our model catalogue.

Upstream: scikit-fingerprints/MoleculeNet_SIDER - 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 - Protein Target Explorer - Drug Discovery Model Hub

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