README.md

---
language:
- en
tags:
- bioassay
pretty_name: ChAFF
size_categories:
- 100K<n<1M
dataset_info:
  - config_name: ChAFF
    features:
    - name: Type
      dtype: string
    - name: DatasetName
      dtype: string
    - name: AID
      dtype: string
    - name: ID
      dtype: string
    - name: IDType
      dtype: string
    - name: SMILES
      dtype: string
---

# ChAFF datasets
This dataset collection contains ~200K curated Active compound lists from ~90 different BioAssay datasets, focusing on known assay interference artifacts. We applied SMILES standardization using RDKit and MolVS, including molecule sanitization and fragment removal. The final dataset is suitable for training and evaluating machine learning models.

## Types and Number of Active Compounds
| Type                 | NumActiveCompounds |
-----------------------|--------------------| 
| Absorbance           | 1486               |
| Artifact             | 10952              |
| Autofluoresence      | 32054              |
| ColloidalAggregators | 19553              |
| HeavyHitters         | 71981              |
| LuciferaseInhibition | 32831              |
| Misannotation        | 39                 |
| Reactivity           | 3107               |
| REDOX                | 217                |


## Dataset Columns
| Column     | Description                           |
|------------|-------------------------------------- |
| Type       | Task domain (e.g. Absorbance)         |
| DatasetName| Source dataset name                   |
| AID        | Pubchem Assay ID                      |
| ID         | Identifier for the compound           |
| IDType     | Type of identifier (e.g. CID)         |
| SMILES     | Curated SMILES                        |

Datasets can be found in the [data folder](https://huggingface.co/datasets/maomlab/ChAFF/tree/main/data).


## Dataset summary
A summary file is uploaded, which lists:
- Type
- DatasetName
- AID
- NumActiveCompounds
- PaperTitle
- Reference
- URL
- AssayName
- Description

Dataset summary file can be found: ChAFF_dataset_summary.json


# License
Each dataset comes from different sources (i.e., PubChem, Papers). Please check our dataset summary file if you are looking for references.


## 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
    >>> from datasets import load_dataset, Features, Value

Specifiy column types to prevent pyarrow error.
```python
features = Features({
    "Type": Value("string"),
    "DatasetName": Value("string"),
    "AID": Value("string"), # Treat int as string
    "ID": Value("string"),
    "IDType": Value("string"),
    "SMILES": Value("string")
})
```

Now load one of the 'ChAFF' datasets, e.g.,

    >>> dataset = datasets.load_dataset("maomlab/ChAFF", name = "default", data_files = "data/Absorbance.csv", split = "train", features = features)


You can modify "data/Absorbance.csv" based on your interest (e.g., "data/Reactivity.csv"). 
The default is split = "train" as we did not split the datasets.