README.md

metadata

license: mit
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - HTS
pretty_name: Assay-Interfering-Compounds Finder
size_categories:
  - 1M<n<10M
dataset_summary: >-
  The assay-interfering-compounds finder consists of 17 different datasets. The
  datasets are uploaded after molecular sanitization using RDKit and MolVS.
citation: |-
  @article{Boldini2024,
    title = {Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery},
    ISSN = {2374-7951},
    url = {http://dx.doi.org/10.1021/acscentsci.3c01517},
    DOI = {10.1021/acscentsci.3c01517},
    journal = {ACS Central Science},
    publisher = {American Chemical Society (ACS)},
    author = {Boldini,  Davide and Friedrich,  Lukas and Kuhn,  Daniel and Sieber,  Stephan A.},
    year = {2024},
    month = mar 
  }
config_names:
  - Boldini2024
configs:
  - config_name: Boldini2024
    data_files:
      - GPCR.csv
      - GPCR2.csv
      - GPCR3.csv
      - channel_atp.csv
      - cysteine_protease.csv
      - IonChannel.csv
      - IonChannel2.csv
      - IonChannel3.csv
      - kinase.csv
      - serine.csv
      - splicing.csv
      - transcrption.csv
      - transcription2.csv
      - transcription3.csv
      - transporter.csv
      - ubiquitin.csv
      - zinc_finger.csv
dataset_info:
  - config_name: GPCR_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: GPCR2_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: GPCR3_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: channel_atp_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: cysteine_protease_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: IonChannel_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: IonChannel2_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: IonChannel3_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: kinase_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: serine_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: splicing_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: transcription_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: transcription2_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: transcription3_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: transporter_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: ubiquitin_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64
  - config_name: zinc_finger_sanitized
    features:
      - name: SMILES
        dtype: string
      - name: Primary
        dtype: int64
      - name: Score
        dtype: float64
      - name: Confirmatory
        dtype: float64

Boldini2024 (Assay-Interfering-Compounds Finder)

17 Datasets that are used to employ Minimum Variance Sampling Analysis (MVS-A) to find Assay Interfering Compounds (AIC) in High Throughput Screening data. In this study, they present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. Their method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign.

The datasets uploaded to our Hugging Face repository have been sanitized using RDKit and MolVS. If you want to try these processes with the original dataset, please follow the instructions in the Processing Script.py file in the maomlab/Boldini2024.

Citation

ACS Cent. Sci. 2024, 10, 4, 823–832 Publication Date:March 15, 2024 https://doi.org/10.1021/acscentsci.3c01517