Datasets:

Aurigene-AI
/

MoleculeNet_BACE

license: unknown
task_categories:
  - tabular-classification
  - graph-ml
  - text-classification
tags:
  - chemistry
  - biology
  - medical
  - binding-affinity-prediction
  - molecular-property-prediction
  - drug-discovery
  - aurigene
pretty_name: MoleculeNet BACE
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: bace.csv

Mirrored by Aurigene AI

Discovery stage: Lead optimization

BACE-1 (beta-secretase 1) inhibition, a validated Alzheimer's disease target. Binary classification.

Rows: 1,513 (bace.csv 1,513)

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

Upstream: scikit-fingerprints/MoleculeNet_BACE - 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 BACE

BACE dataset [1], part of MoleculeNet [2] benchmark. It is intended to be used through scikit-fingerprints library.

The task is to predict binding results for a set of inhibitors of humanβ-secretase 1 (BACE-1).

Characteristic	Description
Tasks	1
Task type	classification
Total samples	1513
Recommended split	scaffold
Recommended metric	AUROC

References

[1] Govindan Subramanian et al. "Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches" J. Chem. Inf. Model. 2016, 56, 10, 1936–1949 https://pubs.acs.org/doi/10.1021/acs.jcim.6b00290

[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