---
license: cc-by-sa-4.0
task_categories:
- tabular-classification
language:
- en
tags:
- molecular data
- few-shot learning
pretty_name: FS-Mol
size_categories:
- 1M<n<10M
dataset_summary: FSMol is a dataset curated from ChEMBL27 for small molecule activity prediction. 
 It consists of 5,120 distinct assays and includes a total of 233,786 unique compounds.
citation: "
  @article{stanley2021fs,
  title={FS-Mol: A Few-Shot Learning Dataset of Molecules},
  author={Stanley, Matthew and Ramsundar, Bharath and Kearnes, Steven and Riley, Patrick},
  journal={NeurIPS 2021 AI for Science Workshop},
  year={2021},
  url={https://www.microsoft.com/en-us/research/publication/fs-mol-a-few-shot-learning-dataset-of-molecules/}
} "
configs:
- config_name: FSMol
  data_files:
  - split: train
    path: FSMol/train-*
  - split: test
    path: FSMol/test-*
  - split: validation
    path: FSMol/validation-*
dataset_info:
  config_name: FSMol
  features:
  - name: SMILES
    dtype: string
  - name: 'Y'
    dtype: int64
    description: 'Binary classification (0/1)' 
  - name: Assay_ID
    dtype: string
  - name: RegressionProperty
    dtype: float64
  - name: LogRegressionProperty
    dtype: float64
  - name: Relation
    dtype: string
  splits:
  - name: train
    num_bytes: 491639392
    num_examples: 5038727
  - name: test
    num_bytes: 5361196
    num_examples: 56220
  - name: validation
    num_bytes: 1818181
    num_examples: 19008
  download_size: 154453519
  dataset_size: 498818769

---
# FS-Mol

[FS-Mol](https://www.microsoft.com/en-us/research/publication/fs-mol-a-few-shot-learning-dataset-of-molecules/) is a dataset curated from ChEMBL27 for small molecule activity prediction. 
It consists of 5,120 distinct assays and includes a total of 233,786 unique compounds.
This is a mirror of the [Official Github repo](https://github.com/microsoft/FS-Mol) where the dataset was uploaded in 2021.


## Preprocessing

We utilized the raw data uploaded on [Github](https://github.com/microsoft/FS-Mol) and performed several preprocessing:
1. Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
2. Formatting (Combine jsonl.gz files to one csv/parquet file)
3. Rename the columns ('Property' to 'Y')
4. Convert the floats in 'Y' column to integers 
5. Split the dataset (train, test, validation)

If you would like to try our pre-processing steps, run our [script](https://huggingface.co/datasets/maomlab/FSMol/blob/main/FSMol_preprocessing.py). 


## 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 `FSMol` datasets, e.g.,

    >>> FSMol = datasets.load_dataset("maomlab/FSMol", name = "FSMol")
    train-00000-of-00001.parquet: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 152M/152M [00:03<00:00, 39.4MB/s]
    test-00000-of-00001.parquet: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.54M/1.54M [00:00<00:00, 33.3MB/s]
    validation-00000-of-00001.parquet: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 517k/517k [00:00<00:00, 52.6MB/s]
    Generating train split: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5038727/5038727 [00:08<00:00, 600413.56 examples/s]
    Generating test split: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 56220/56220 [00:00<00:00, 974722.00 examples/s]
    Generating validation split: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 19008/19008 [00:00<00:00, 871143.71 examples/s]

and inspecting the loaded dataset

    >>> FSMol
    DatasetDict({
            train: Dataset({
                features: ['SMILES', 'Y', 'Assay_ID', 'RegressionProperty', 'LogRegressionProperty', 'Relation'],
                num_rows: 5038727
            })
            test: Dataset({
                features: ['SMILES', 'Y', 'Assay_ID', 'RegressionProperty', 'LogRegressionProperty', 'Relation'],
                num_rows: 56220
            })
            validation: Dataset({
                features: ['SMILES', 'Y', 'Assay_ID', 'RegressionProperty', 'LogRegressionProperty', 'Relation'],
                num_rows: 19008
            })

    })

### 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/FSMol', name = 'FSMol')
    
    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
@article{stanley2021fs,
title={FS-Mol: A Few-Shot Learning Dataset of Molecules},
author={Stanley, Matthew and Ramsundar, Bharath and Kearnes, Steven and Riley, Patrick},
journal={NeurIPS 2021 AI for Science Workshop},
year={2021},
url={https://www.microsoft.com/en-us/research/publication/fs-mol-a-few-shot-learning-dataset-of-molecules/