README.md

---
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](https://huggingface.co/datasets/maomlab/Boldini2024/blob/main/Boldini2024%20Preprocessing.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