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.
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 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.
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.