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 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 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} }