license:mit size_categories:1K<n<10K format:parquet modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: mit
configs:
- config_name: Simeonov2008
  data_files:
  - split: train
    path: Simeonov2008/train-*
dataset_info:
  config_name: Simeonov2008
  features:
  - name: AID
    dtype: int64
  - name: SID
    dtype: int64
  - name: SID Group
    dtype: int64
  - name: CID
    dtype: int64
  - name: Activity Outcome
    dtype: string
  - name: Activity Name
    dtype: float64
  - name: Activity Qualifier
    dtype: float64
  - name: Activity Value
    dtype: float64
  - name: Protein Accession
    dtype: float64
  - name: Gene ID
    dtype: float64
  - name: Target TaxID
    dtype: float64
  - name: PMID
    dtype: float64
  - name: smiles
    dtype: string
  - name: InChI
    dtype: string
  splits:
  - name: train
    num_bytes: 1989642
    num_examples: 7152
  download_size: 838192
  dataset_size: 1989642
---
# Simeonov2008
The Simeonov2008 dataset contains 7,152 compounds in the train set, with high-throughput screening (HTS) results recorded in the "Activity Outcome" column. 


## Quickstart Usage

### Load a dataset in python
Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
First, from the command line install the `datasets` library

    $ pip install datasets

then, from within python load the datasets library

    >>> import datasets
   
and load the `Simeonov2008` datasets, e.g.,

    >>> Simeonov2008 = datasets.load_dataset('haneulpark/Simeonov2008')
        README.md: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 878/878 [00:00<00:00, 115kB/s]
        train-00000-of-00001.parquet: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 838k/838k [00:00<00:00, 23.0MB/s]
        Generating train split: 100%|██████████████████████████████████████████████████████████████████████████████████████| 7152/7152 [00:00<00:00, 150946.82 examples/s]

and inspecting the loaded dataset

    >>> Simeonov2008
        DatasetDict({
            train: Dataset({
                features: ['AID', 'SID', 'SID Group', 'CID', 'Activity Outcome', 'Activity Name', 'Activity Qualifier', 'Activity Value', 'Protein Accession', 'Gene ID', 'Target TaxID', 'PMID', 'smiles', 'InChI'],
                num_rows: 7152
            })
        })

### Use a dataset to train a model
One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support

    pip install 'molflux[catboost,rdkit]'

then load, featurize, split, fit, and evaluate the catboost model

    import json
    from datasets import load_dataset
    from molflux.datasets import featurise_dataset
    from molflux.features import load_from_dicts as load_representations_from_dicts
    from molflux.splits import load_from_dict as load_split_from_dict
    from molflux.modelzoo import load_from_dict as load_model_from_dict
    from molflux.metrics import load_suite

Split and evaluate the catboost model
    
    split_dataset = load_dataset('haneulpark/Simeonov2008')
    
    split_featurised_dataset = featurise_dataset(
      split_dataset,
      column = "smiles",
      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))

    model = load_model_from_dict({
        "name": "cat_boost_classifier",
        "config": {
            "x_features": ['smiles::morgan', 'smiles::maccs_rdkit'],
            "y_features": ['Activity Outcome']}})
    
    model.train(split_featurised_dataset["train"])
    preds = model.predict(split_featurised_dataset["test"])
    
    classification_suite = load_suite("classification")
    
    scores = classification_suite.compute(
        references=split_featurised_dataset["test"]['Activity Outcome'],
        predictions=preds["cat_boost_classifier::Activity Outcome"])  

### Citation
@article{Simeonov2008,
  title = {Fluorescence Spectroscopic Profiling of Compound Libraries},
  volume = {51},
  ISSN = {1520-4804},
  url = {http://dx.doi.org/10.1021/jm701301m},
  DOI = {10.1021/jm701301m},
  number = {8},
  journal = {Journal of Medicinal Chemistry},
  publisher = {American Chemical Society (ACS)},
  author = {Simeonov,  Anton and Jadhav,  Ajit and Thomas,  Craig J. and Wang,  Yuhong and Huang,  Ruili and Southall,  Noel T. and Shinn,  Paul and Smith,  Jeremy and Austin,  Christopher P. and Auld,  Douglas S. and Inglese,  James},
  year = {2008},
  month = mar,
  pages = {2363–2371}
}


        