README.md

---
tags:
- chemistry
- chemical information
pretty_name: Hematotoxicity Dataset
dataset_summary: >- 
 The hematotoxicity dataset consists of a training set with 1788 molecules and a test set with 594 molecules. 
 The train and test datasets were created after sanitizing and splitting the original dataset in the paper below.
citation: >-
  @article{,
    author = {Teng-Zhi Long, Shao-Hua Shi, Shao Liu, Ai-Ping Lu, Zhao-Qian Liu, Min Li, Ting-Jun Hou*, and Dong-Sheng Cao},
    doi = {10.1021/acs.jcim.2c01088},
    journal = {Journal of Chemical Information and Modeling},
    number = {1},
    title = {Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches},
    volume = {63},
    year = {2023},
    url = {https://pubs.acs.org/doi/10.1021/acs.jcim.2c01088},
    publisher = {ACS publications}
  }
size_categories:
- 1K<n<10K
config_names:
- HematoxLong2023
configs:
- config_name: HematoxLong2023
  data_files:
  - split: test
    path: HematoxLong2023/test.csv
  - split: train
    path: HematoxLong2023/train.csv
dataset_info:
- config_name: HematoxLong2023
  features:
    - name: "new SMILES"
      dtype: string
    - name: "Label"
      dtype: int64
  splits:
    - name: train
      num_bytes: 28736
      num_examples: 1788
    - name: test
      num_bytes: 9632
      num_examples: 594
---

# Hematotoxicity Dataset (HematoxLong2023)
A hematotoxicity dataset containing 1772 chemicals was obtained, which includes a positive set with 589 molecules and a negative set with 1183 molecules.
The molecules were divided into a training set of 1330 molecules and a test set of 442 molecules according to their Murcko scaffolds. 
Additionally, 610 new molecules from related research and databases were compiled as the external validation set.

The train and test datasets uploaded to our Hugging Face repository have been sanitized and split from the original dataset, which contains 2382 molecules. 
If you would like to try these processes with the original dataset, please follow the instructions in the [Processing Script.py](https://huggingface.co/datasets/maomlab/HematoxLong2023/blob/main/Processing%20Script.py) file located in the HematoxLong2023 folder.

## 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 one of the `HematoxLong2023` datasets, e.g.,

    >>> HematoxLong2023 = datasets.load_dataset("maomlab/HematoxLong2023", name = "HematoxLong2023")
    Downloading readme: 100%|██████████| 5.23k/5.23k [00:00<00:00, 35.1kkB/s]
    Downloading data: 100%|██████████| 34.5k//34.5k/ [00:00<00:00, 155kB/s]
    Downloading data: 100%|██████████| 97.1k/97.1k [00:00<00:00, 587kB/s]
    Generating test split: 100%|██████████| 594/594 [00:00<00:00, 12705.92 examples/s]
    Generating train split: 100%|██████████| 1788/1788 [00:00<00:00, 43895.91 examples/s]

and inspecting the loaded dataset

    >>> HematoxLong2023
    HematoxLong2023
    DatasetDict({
      test: Dataset({
         features: ['new SMILES', 'label'],
         num_rows: 594
      })
      train: Dataset({
          features: ['new SMILES', 'label'],
          num_rows: 1788
      })    
    })

### 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('maomlab/HematoxLong2023', name = 'HematoxLong2023')
    
    split_featurised_dataset = featurise_dataset(
      split_dataset,
      column = "new SMILES",
      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))

    model = load_model_from_dict({
        "name": "cat_boost_classifier",
        "config": {
            "x_features": ['new SMILES::morgan', 'new SMILES::maccs_rdkit'],
            "y_features": ['Label']}})
    
    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"]['Label'],
        predictions=preds["cat_boost_classifier::Label"])        

## Citation
Cite this:
J. Chem. Inf. Model. 2023, 63, 1, 111–125
Publication Date:December 6, 2022
https://doi.org/10.1021/acs.jcim.2c01088
Copyright © 2024 American Chemical Society