---
language: en
source_datasets: curated
license: mit
tags:
- chemistry
- toxicology
pretty_name: Human & Rat Liver Microsomal Stability
dataset_summary: >-
  Curation of databases of compounds for assessing human liver microsomes (HLM)
  stability and rat liver microsomes (RLM) stability.
citation: |-
  @article{
    author = {Longqiang Li, Zhou Lu, Guixia Liu, Yun Tang, and Weihua Li},
    doi = {10.1021/acs.chemrestox.2c00207},
    journal = {Chemical Research in Toxicology},
    number = {9},
    title = {In Silico Prediction of Human and Rat Liver Microsomal Stability via Machine Learning Methods},
    volume = {35},
    year = {2022},
    url = {https://pubs.acs.org/doi/10.1021/acs.chemrestox.2c00207},
    publisher = {American Chemical Society}
  }
size_categories:
- 10K<n<100K
config_names:
- HLM
- RLM
- Marketed_Drug
configs:
- config_name: HLM
  data_files:
  - split: test
    path: HLM/test.csv
  - split: train
    path: HLM/train.csv
  - split: external
    path: HLM/external.csv
- config_name: RLM
  data_files:
  - split: test
    path: RLM/test.csv
  - split: train
    path: RLM/train.csv
  - split: external
    path: RLM/external.csv
- config_name: Marketed_Drug
dataset_info:
- config_name: HLM
  features:
  - name: ID
    dtype: string
  - name: SMILES
    dtype: string
  - name: Y
    dtype:
      class_label:
        names:
          '0': 0
          '1': 1
  splits:
  - name: train
    num_bytes: 190968
    num_examples: 4771
  - name: test
    num_bytes: 45368
    num_examples: 1131
  - name: external
    num_bytes: 4568
    num_examples: 111
- config_name: RLM
  features:
  - name: ID
    dtype: string
  - name: SMILES
    dtype: string
  - name: Y
    dtype:
      class_label:
        names:
          '0': 0
          '1': 1
  splits:
  - name: train
    num_bytes: 100608
    num_examples: 2512
  - name: test
    num_bytes: 23968
    num_examples: 596
  - name: external
    num_bytes: 99408
    num_examples: 2484
- config_name: Marketed_Drug
  features:
  - name: SMILES
    dtype: string
  - name: Class
    dtype:
      class_label:
        names:
          '0': 0
          '1': 1
  - name: Online server predicted class
    dtype:
      class_label:
        names:
          '0': 0
          '1': 1
  - name: Our predicted class
    dtype:
      class_label:
        names:
          '0': 0
          '1': 1
task_categories:
- tabular-classification
---

# Human & Rat Liver Microsomal Stability
3345 RLM and 6420 HLM compounds were initially collected from the ChEMBL bioactivity database. 
(HLM ID: 613373, 2367379, and 612558; RLM ID: 613694, 2367428, and 612558)
Finally, the RLM stability data set contains 3108 compounds, and the HLM stability data set contains 5902 compounds.
For the RLM stability data set, 1542 (49.6%) compounds were classified as stable, and 1566 (50.4%) compounds were classified as unstable, 
among which the training and test sets contain 2512 and 596 compounds, respectively. 
The experimental data from the National Center for Advancing Translational Sciences (PubChem AID 1508591) were used as the RLM external set. 
For the HLM data set, 3799 (64%) compounds were classified as stable, and 2103 (36%) compounds were classified as unstable. 
In addition, an external set from Liu et al.12 was used to evaluate the predictive power of the HLM model.

The datasets uploaded to our Hugging Face repository are sanitized and reorganized versions. 
(We have sanitized the molecules from the original paper, using MolVS.)


## 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 `HLM_RLM` datasets, e.g.,

    >>> HLM = datasets.load_dataset("maomlab/HLM_RLM", name = "HLM")
    Downloading readme: 100%|████████████████████████| 6.93k/6.93k [00:00<00:00, 280kB/s]
    Downloading data: 100%|██████████████████████████| 680k/680k [00:00<00:00, 946kB/s]
    Downloading data: 100%|██████████████████████████| 925k/925k [00:01<00:00, 634kB/s]
    Downloading data: 100%|██████████████████████████| 39.7k/39.7k [00:00<00:00, 90.8kB/s]
    Generating test split: 100%|█████████████████████| 1131/1131 [00:00<00:00, 20405.98 examples/s]
    Generating train split: 100%|████████████████████| 4771/4771 [00:00<00:00, 65495.46 examples/s]
    Generating external split: 100%|████████████████████| 111/111 [00:00<00:00, 3651.94 examples/s]

and inspecting the loaded dataset

    >>> HLM
    HLM
    DatasetDict({
      test: Dataset({
         features: ['ID','SMILES', 'Y'],
         num_rows: 1131
      })
      train: Dataset({
          features: ['ID','SMILES', 'Y'],
          num_rows: 4771
      }) 
      external: Dataset({
          features: ['ID','SMILES', 'Y'],
          num_rows: 111          
      })
    })

### 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 a 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_dataset = load_dataset('maomlab/HLM_RLM', name = 'HLM')
    
    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": ['Y'],
        }})
        
    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"]['Y'],
        predictions=preds["cat_boost_classifier::Y"])        

## Citation
Chem. Res. Toxicol. 2022, 35, 9, 1614–1624
Publication Date:September 2, 2022
https://doi.org/10.1021/acs.chemrestox.2c00207