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Datasets:
maomlab
/
Liability

Tasks:
Tabular Classification
Tabular Regression
Languages:
English
Size:
10K<n<100K
Tags:
HTS
Medicinal Chemistry
License:
Dataset card Files Community
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- ---
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- license: mit
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ task_categories:
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+ - tabular-classification
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+ - tabular-regression
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+ language:
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+ - en
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+ tags:
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+ - HTS
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+ - Medicinal Chemistry
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+ pretty_name: Liability
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+ size_categories:
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+ - 10K<n<100K
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+ dataset_summery: >-
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+ HTS datasets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity, and each dataset contains ~5000 compounds.
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+ We have sanitized the datasets from the paper below and uploaded them to our Hugging Face repository.
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+ citation: >-
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+ @article{Willson2023,
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+ author = {Thomas M. Willson and Matthew G. Metzger and Regina A. Buchthal and Patrick R. Griffin},
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+ title = {Identifying and Mitigating False Positives in High-Throughput Screening},
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+ journal = {Journal of Medicinal Chemistry},
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+ year = {2023},
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+ volume = {66},
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+ number = {14},
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+ pages = {12345-12356},
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+ doi = {10.1021/acs.jmedchem.3c00482},
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+ url = {https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c00482}
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+ }
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+ config_names:
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+ - Firefly Luciferase Interference
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+ - MSTI Thiol Interference
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+ - Nano Luciferase Interference
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+ - REDOX Interference
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+ configs:
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+ - config_name: Firefly Luciferase Interference
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+ - config_name: MSTI Thiol Interference
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+ - config_name: Nano Luciferase Interference
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+ - config_name: REDOX Interference
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+ dataset_info:
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+ - config_name: Firefly Luciferase Interference
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+ features:
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+ - name: "REGID_1"
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+ dtype: string
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+ - name: "REGID_2"
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+ dtype: string
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+ - name: "REGID_3"
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+ dtype: string
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+ - name: "newSMILES_1"
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+ dtype: string
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+ - name: "newSMILES_2"
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+ dtype: string
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+ - name: "newSMILES_3"
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+ dtype: string
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+ - name: "log_AC50_M"
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+ dtype: float64
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+ - name: "Efficacy"
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+ dtype: float64
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+ - name: "CC-v2"
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+ dtype: float64
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+ - name: "Outcome"
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+ dtype: int64
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+ - name: "InChIKey"
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+ dtype: string
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+ - name: "ID"
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+ dtype: float64
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+ - name: "ROMol"
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+ dtype: string
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+ - config_name: MSTI Thiol Interference
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+ features:
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+ - name: "REGID_1"
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+ dtype: string
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+ - name: "REGID_2"
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+ dtype: string
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+ - name: "REGID_3"
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+ dtype: string
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+ - name: "newSMILES_1"
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+ dtype: string
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+ - name: "newSMILES_2"
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+ dtype: string
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+ - name: "newSMILES_3"
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+ dtype: string
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+ - name: "log_AC50_M"
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+ dtype: float64
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+ - name: "Efficacy"
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+ dtype: float64
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+ - name: "CC-v2"
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+ dtype: float64
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+ - name: "Outcome"
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+ dtype: int64
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+ - name: "InChIKey"
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+ dtype: string
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+ - name: "ID"
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+ dtype: float64
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+ - name: "ROMol"
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+ dtype: string
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+ - config_name: Nano Luciferase Interference
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+ features:
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+ - name: "REGID_1"
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+ dtype: string
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+ - name: "REGID_2"
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+ dtype: string
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+ - name: "REGID_3"
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+ dtype: string
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+ - name: "newSMILES_1"
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+ dtype: string
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+ - name: "newSMILES_2"
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+ dtype: string
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+ - name: "newSMILES_3"
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+ dtype: string
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+ - name: "log_AC50_M"
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+ dtype: float64
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+ - name: "Efficacy"
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+ dtype: float64
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+ - name: "CC-v2"
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+ dtype: float64
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+ - name: "Outcome"
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+ dtype: int64
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+ - name: "InChIKey"
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+ dtype: string
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+ - name: "ID"
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+ dtype: float64
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+ - name: "ROMol"
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+ dtype: string
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+ - config_name: REDOX Interference
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+ features:
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+ - name: "REGID_1"
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+ dtype: string
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+ - name: "REGID_2"
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+ dtype: string
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+ - name: "newSMILES_1"
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+ dtype: string
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+ - name: "newSMILES_2"
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+ dtype: string
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+ - name: "log_AC50_M"
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+ dtype: float64
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+ - name: "Efficacy"
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+ dtype: float64
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+ - name: "CC-v2"
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+ dtype: float64
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+ - name: "Outcome"
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+ dtype: int64
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+ - name: "InChIKey"
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+ dtype: string
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+ - name: "ID"
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+ dtype: float64
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+ - name: "ROMol"
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+ dtype: string
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+ ---
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+ # Liability (Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds)
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+ “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.
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+
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+ The datasets uploaded to our Hugging Face repository have been sanitized using RDKit and MolVS.
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+ If you want to try these processes with the original dataset, please follow the instructions in the [Processing Script.py]() file in the maomlab/Liability.
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+
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+ 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.
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+ 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.
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+
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+
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+ # Citation
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+ ACS Cent. Sci. 2024, 10, 4, 823–832
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+ Publication Date:March 15, 2024
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+ https://doi.org/10.1021/acscentsci.3c01517