---
version: 1.0.0
license: cc-by-nc-4.0
task_categories:
- tabular-regression
language:
- en
tags:
- IUPAC
- pKa
pretty_name: IUPAC_pKa
size_categories:
- 10K<n<100K
dataset_summary: Curated dataset of pKa values digitized from three IUPAC reference books with 10,624 uniqe molecules.
citation: https://doi.org/10.5281/zenodo.7236453
dataset_info:
- config_name: IUPAC_pKa
  features:
  - name: unique_ID
    dtype: string
  - name: SMILES
    dtype: string
  - name: InChI
    dtype: string
  - name: pka_type
    dtype: string
  - name: Y
    dtype: float64
  - name: T
    dtype: string
  - name: remarks
    dtype: string
  - name: method
    dtype: string
  - name: assessment
    dtype: string
  - name: ref
    dtype: string
  - name: ref_remarks
    dtype: string
  - name: entry_remarks
    dtype: string
  - name: original_IUPAC_names
    dtype: string
  - name: name_contributors
    dtype: string
  - name: num_name_contributors
    dtype: int64
  - name: original_IUPAC_nicknames
    dtype: string
  - name: source
    dtype: string
  - name: pressure
    dtype: string
  - name: acidity_label
    dtype: string
  - name: original_T
    dtype: string
  - name: solvent
    dtype: string
  - name: ClusterNo
    dtype: int64
  - name: MolCount
    dtype: int64
  - name: group
    dtype: string
  splits:
  - name: train
    num_bytes: 6849049
    num_examples: 18168
  - name: test
    num_bytes: 2191280
    num_examples: 6054
  download_size: 1465758
  dataset_size: 9040329
- config_name: default
  features:
  - name: unique_ID
    dtype: string
  - name: SMILES
    dtype: string
  - name: InChI
    dtype: string
  - name: pka_type
    dtype: string
  - name: Y
    dtype: float64
  - name: T
    dtype: string
  - name: remarks
    dtype: string
  - name: method
    dtype: string
  - name: assessment
    dtype: string
  - name: ref
    dtype: string
  - name: ref_remarks
    dtype: string
  - name: entry_remarks
    dtype: string
  - name: original_IUPAC_names
    dtype: string
  - name: name_contributors
    dtype: string
  - name: num_name_contributors
    dtype: int64
  - name: original_IUPAC_nicknames
    dtype: string
  - name: source
    dtype: string
  - name: pressure
    dtype: string
  - name: acidity_label
    dtype: string
  - name: original_T
    dtype: string
  - name: solvent
    dtype: string
  - name: ClusterNo
    dtype: int64
  - name: MolCount
    dtype: int64
  - name: group
    dtype: string
  splits:
  - name: train
    num_bytes: 6849049
    num_examples: 18168
  - name: test
    num_bytes: 2191280
    num_examples: 6054
  download_size: 1465758
  dataset_size: 9040329
configs:
- config_name: IUPAC_pKa
  data_files:
  - split: train
    path: IUPAC_pKa/train-*
  - split: test
    path: IUPAC_pKa/test-*
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# IUPAC_pKa
IUPAC_pKa dataset contains high-confidence pKa values digitized from three IUPAC reference books, with chemical identifiers (SMILES, InChI) and metadata on acidity, temperature, solvent, and measurement methods. 
The dataset consists of 24,222 rows corresponding to 10,624 uniqe molecules.
This is a mirror of the [Official Github repo](https://github.com/IUPAC/Dissociation-Constants?tab=readme-ov-file) where the dataset v2_2 was uploaded.


## Preprocessing
We utilized the raw data uploaded on [Github](https://github.com/IUPAC/Dissociation-Constants?tab=readme-ov-file) and performed several preprocessing:
1. Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
2. Rename the columns
3. Split the dataset (train, test, validation)

If you would like to try these processes with the original dataset, 
please follow the instructions in the [preprocessing script](https://huggingface.co/datasets/maomlab/IUPAC_pKa/blob/main/IUPAC_pKa_preprocessing.py)
file located in our IUPAC_pKa repository.


### Data splits
The original IUPAC_pKa dataset does not define splits, so here we have used the 'Realistic Split' method described in [Martin et al., 2018](https://pubs.acs.org/doi/10.1021/acs.jcim.7b00166).


## 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 `IUPAC_pKa` datasets, e.g.,

    >>> IUPAC_pKa = datasets.load_dataset('maomlab/IUPAC_pKa')
    README.md: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.06k/5.06k [00:00<00:00, 771kB/s]
    train-00000-of-00001.parquet: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 947k/947k [00:00<00:00, 34.0MB/s]
    test-00000-of-00001.parquet: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 519k/519k [00:00<00:00, 23.5MB/s]
    Generating train split: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 18168/18168 [00:00<00:00, 260823.23 examples/s]
    Generating test split: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 6054/6054 [00:00<00:00, 231724.00 examples/s]

and inspecting the loaded dataset

    >>> IUPAC_pKa
    DatasetDict({
        train: Dataset({
            features: ['unique_ID', 'SMILES', 'InChI', 'pka_type', 'Y', 'T', 'remarks', 'method', 'assessment', 'ref', 'ref_remarks', 'entry_remarks', 'original_IUPAC_names', 'name_contributors', 'num_name_contributors', 'original_IUPAC_nicknames', 'source', 'pressure', 'acidity_label', 'original_T', 'solvent', 'ClusterNo', 'MolCount', 'group'],
            num_rows: 18168
        })
        test: Dataset({
            features: ['unique_ID', 'SMILES', 'InChI', 'pka_type', 'Y', 'T', 'remarks', 'method', 'assessment', 'ref', 'ref_remarks', 'entry_remarks', 'original_IUPAC_names', 'name_contributors', 'num_name_contributors', 'original_IUPAC_nicknames', 'source', 'pressure', 'acidity_label', 'original_T', 'solvent', 'ClusterNo', 'MolCount', 'group'],
            num_rows: 6054
        })
    })

### 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_dataset = load_dataset('maomlab/IUPAC_pKa')
    
    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_regressor",
        "config": {
            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
            "y_features": ['Y']}})
    
    model.train(split_featurised_dataset["train"])
    preds = model.predict(split_featurised_dataset["test"])
    
    regression_suite = load_suite("regression")
    
    scores = regression_suite.compute(
        references=split_featurised_dataset["test"]['Y'],
        predictions=preds["cat_boost_regressor::Y"])



### Citation
Zheng, Jonathan W. and Lafontant-Joseph, Olivier. (2024) IUPAC Digitized pKa Dataset, v2.2. 
Copyright © 2024 International Union of Pure and Applied Chemistry (IUPAC), 
The dataset is reproduced by permission of IUPAC and is licensed under a CC BY-NC 4.0. 
Access at https://doi.org/10.5281/zenodo.7236453.