---
license: mit
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- HTS
- Medicinal Chemistry
pretty_name: Liability
size_categories:
- 10K<n<100K
dataset_summery: >-
  HTS datasets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity, and each dataset contains ~5000 compounds.
  We have sanitized the datasets from the paper below and uploaded them to our Hugging Face repository.
citation: >-
  @article{Willson2023,
    author = {Thomas M. Willson and Matthew G. Metzger and Regina A. Buchthal and Patrick R. Griffin},
    title = {Identifying and Mitigating False Positives in High-Throughput Screening},
    journal = {Journal of Medicinal Chemistry},
    year = {2023},
    volume = {66},
    number = {14},
    pages = {12345-12356},
    doi = {10.1021/acs.jmedchem.3c00482},
    url = {https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482}
  }
config_names:
- Liability
configs:
- config_name: Liability
  data_files:
  - Firefly Luciferase Interference.csv
  - MSTI Thiol Interference.csv
  - Nano Luciferase Interference.csv
  - REDOX Interference.csv
dataset_info:
- config_name: Firefly Luciferase Interference
  features:
    - name: "REGID_1"
      dtype: string
    - name: "REGID_2"
      dtype: string
    - name: "REGID_3"
      dtype: string
    - name: "SMILES_1"
      dtype: string
    - name: "SMILES_2"
      dtype: string
    - name: "SMILES_3"
      dtype: string
    - name: "log_AC50_M"
      dtype: float64
    - name: "Efficacy"
      dtype: float64
    - name: "CC-v2"
      dtype: float64
    - name: "Outcome"
      dtype: int64
    - name: "InChIKey"
      dtype: string
    - name: "ID"
      dtype: float64
    - name: "ROMol"
      dtype: string
- config_name: MSTI Thiol Interference
  features:
    - name: "REGID_1"
      dtype: string
    - name: "REGID_2"
      dtype: string
    - name: "REGID_3"
      dtype: string
    - name: "SMILES_1"
      dtype: string
    - name: "SMILES_2"
      dtype: string
    - name: "SMILES_3"
      dtype: string
    - name: "log_AC50_M"
      dtype: float64
    - name: "Efficacy"
      dtype: float64
    - name: "CC-v2"
      dtype: float64
    - name: "Outcome"
      dtype: int64
    - name: "InChIKey"
      dtype: string
    - name: "ID"
      dtype: float64
    - name: "ROMol"
      dtype: string      
- config_name: Nano Luciferase Interference
  features:
    - name: "REGID_1"
      dtype: string
    - name: "REGID_2"
      dtype: string
    - name: "REGID_3"
      dtype: string
    - name: "SMILES_1"
      dtype: string
    - name: "SMILES_2"
      dtype: string
    - name: "SMILES_3"
      dtype: string
    - name: "log_AC50_M"
      dtype: float64
    - name: "Efficacy"
      dtype: float64
    - name: "CC-v2"
      dtype: float64
    - name: "Outcome"
      dtype: int64
    - name: "InChIKey"
      dtype: string
    - name: "ID"
      dtype: float64
    - name: "ROMol"
      dtype: string
- config_name: REDOX Interference
  features:
    - name: "REGID_1"
      dtype: string
    - name: "REGID_2"
      dtype: string
    - name: "SMILES_1"
      dtype: string
    - name: "SMILES_2"
      dtype: string
    - name: "log_AC50_M"
      dtype: float64
    - name: "Efficacy"
      dtype: float64
    - name: "CC-v2"
      dtype: float64
    - name: "Outcome"
      dtype: int64
    - name: "InChIKey"
      dtype: string
    - name: "ID"
      dtype: float64
    - name: "ROMol"
      dtype: string
---
# Liability (Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds)
“Liability Predictor,” a free web tool to predict HTS artifacts has been created with HTS datasets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity, and each dataset contains ~5000 compounds.

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/Liability/tree/main/preprocessing%20scripts) file in the maomlab/Liability.

More specifically, they generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure–interference relationship (QSIR) models to predict these nuisance behaviors. 
Both the models and the curated data sets were implemented in “Liability Predictor,” publicly available at https://liability.mml.unc.edu/. “Liability Predictor” may be used as part of chemical library design or for triaging HTS hits.


# Citation
J. Med. Chem. 2023, 66, 18, 12828–12839
Publication Date:September 7, 2023
https://doi.org/10.1021/acs.jmedchem.3c00482