Datasets:

maomlab
/

FSMol

+---
+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/

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