---
language: en
license: mit
source_datasets: curated
task_categories:
- tabular-regression
tags:
- chemistry
- cheminformatics
pretty_name: Aqueous Solubility Database (AqSolDB)
dataset_summary: >-
  Solubility values of 9,982 unique compounds curated from 9 different publicly
  available aqueous solubility datasets.
citation: >-
  @article{
    author = {Murat Cihan Sorkun, Abhishek Khetan \& Süleyman Er},
    title = {AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds},
    journal = {Scientific Data},
    year = {2019},
    volume = {6},
    number = {143},
    month = {aug},
    url = {https://www.nature.com/articles/s41597-019-0151-1},
    publisher = {Springer Nature}
size_categories:
- 1K<n<10K
config_names:
- AqSolDB
configs:
- config_name: AqSolDB
  data_files:
  - split: test
    path: AqSolDB/test.csv
  - split: train
    path: AqSolDB/train.csv
dataset_info:
- config_name: AqSolDB
  features:    
    - name: "ID"
      dtype: string
    - name: "Name"
      dtype: string
    - name: "InChI"
      dtype: string
    - name: "InChIKey"
      dtype: string
    - name: "SMILES"
      dtype: string
    - name: "Solubility"
      dtype: float64
    - name: "SD"
      dtype: float64
    - name: "Ocurrences"
      dtype: int64
    - name: "Group"
      dtype: string
    - name: "MolWt"
      dtype: float64
    - name: "MolLogP"
      dtype: float64
    - name: "MolMR"
      dtype: float64
    - name: "HeavyAtomCount"
      dtype: float64
    - name: "NumHAcceptors"
      dtype: float64
    - name: "NumHDonors"
      dtype: float64
    - name: "NumHeteroatoms"
      dtype: float64
    - name: "NumRotatableBonds"
      dtype: float64
    - name: "NumValenceElectrons"
      dtype: float64
    - name: "NumAromaticRings"
      dtype: float64
    - name: "NumSaturatedRings"
      dtype: float64
    - name: "NumAliphaticRings"
      dtype: float64
    - name: "RingCount"
      dtype: float64
    - name: "TPSA"
      dtype: float64
    - name: "LabuteASA"
      dtype: float64
    - name: "BalabanJ"
      dtype: float64
    - name: "BertzCT"
      dtype: float64
    - name: "ClusterNo"
      dtype: int64
    - name: "MolCount"
      dtype: int64
    - name: "group"
      dtype: string
  splits:
    - name: train
      num_bytes: 1737344
      num_examples: 7488
    - name: test
      num_bytes: 578736
      num_examples: 2494
---

# Aqueous Solubility Database (AqSolDB)

AqSolDB is created by the Autonomous Energy Materials Discovery [AMD] research group, consists of aqueous solubility values of 
9,982 unique compounds curated from 9 different publicly available aqueous solubility datasets. This openly accessible dataset, 
which is the largest of its kind, and will not only serve as a useful reference source of measured solubility data, but also 
as a much improved and generalizable training data source for building data-driven models.

## 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 one of the `AqSolDB` datasets, e.g.,

    >>> AqSolDB = datasets.load_dataset("maomlab/AqSolDB", name = "AqSolDB")
    Downloading readme: 100%|████████████████████| 10.2k/10.2k [00:00<00:00, 4.41MB/s]
    Downloading data: 100%|█████████████████████████| 972k/972k [00:02<00:00, 432kB/s]
    Downloading data: 100%|██████████████████████| 2.88M/2.88M [00:01<00:00, 1.92MB/s]
    Generating test split: 100%|████████| 2494/2494 [00:00<00:00, 44727.48 examples/s]
    Generating train split: 100%|██████| 7488/7488 [00:00<00:00, 144316.82 examples/s]

and inspecting the loaded dataset

    >>> AqSolDB
    AqSolDB
    DatasetDict({
        test: Dataset({
            features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Solubility', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\
    ectrons', 'NumAromaticRings', 'NumSaturatedRings', 'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ', 'BertzCT', 'ClusterNo', 'MolCount', 'group'],
            num_rows: 2494
        })
        train: Dataset({
            features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Solubility', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\
    ectrons', 'NumAromaticRings', 'NumSaturatedRings', 'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ', 'BertzCT', 'ClusterNo', 'MolCount', 'group'],
            num_rows: 7488
        })
    })

### 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/AqSolDB')
    
    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": ['Solubility']}})
    
    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"]['Solubility'],
        predictions=preds["cat_boost_regressor::Solubility"])    


## Aqueous Solubility Data Curation

### Overview

This repository has been developed in order to curate various aqueous solubility datasets into a broad and extensive dataset called AqSolDB.

The curation process in this work can be accomplished by executing two python scripts in the given sequence:

data-preprocess.py - for pre-processing the raw data set to a standardized format
data-curation.py - for merging the standardized datasets, assigning reliability lables and adding 2D descriptors
These two python scripts call upon functions from other python modules that are defined in:

preprocess.py
merge.py
descriptors.py
Further information about curation process can be found in the associated manuscript.

## Examples

### data-preprocess.py
This file converts 2 example sub-datasets (25 instances from raw forms of dataset-A[1] and dataset-H[6]) 
which are then converted into a standardized format. (This is an example how to preprocess datasets. 
The preporcessed data files already in the data folder.)

inputs:

raw-dataset-A.csv (various solubility metrics (g/L, mg/L..) with Name and CAS Number)
raw-dataset-H.csv (has solubility values(LogS) with SLN representations)

outputs:

dataset-A.csv
dataset-H.csv

Note
To apply this method to your own dataset, perform the following steps:

Check the available properties, representations, and solubility units of your dataset
Select the suitable preprocessing methods from the "preprocess.py" module.


### data-curation.py
This file curates, i.e., merges datasets, selects most reliable values among multiple occurences, 
and adds 2D descriptors from 9 different standardized datasets that are obtained after the pre-processing step.

inputs:

dataset-A.csv [1]
dataset-B.csv [2]
dataset-C.csv [3]
dataset-D.csv [4]
dataset-E.csv [5]
dataset-F.csv [6]
dataset-G.csv [7]
dataset-H.csv [6]
dataset-I.csv [8]

outputs:

dataset_curated.csv

Note
To apply this method, your input dataset should be in the standardized format (output of preprocessing) having following columns:

ID
Name
InChI
InChIKey
SMILES
Solubility
Prediction

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

### Citation
  TY  - JOUR
  AU  - Sorkun, Murat Cihan
  AU  - Khetan, Abhishek
    AU  - Er, S√ºleyman
    PY  - 2019
    DA  - 2019/08/08
TI  - AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds
JO  - Scientific Data
SP  - 143
VL  - 6
IS  - 1
AB  - Water is a ubiquitous solvent in chemistry and life. 
It is therefore no surprise that the aqueous solubility of compounds has a key role in various domains, 
including but not limited to drug discovery, paint, coating, and battery materials design. 
Measurement and prediction of aqueous solubility is a complex and prevailing challenge in chemistry. 
For the latter, different data-driven prediction models have recently been developed to augment the physics-based modeling approaches. 
To construct accurate data-driven estimation models, it is essential that the underlying experimental calibration data used by these models is of high fidelity and quality.
Existing solubility datasets show variance in the chemical space of compounds covered, measurement methods, experimental conditions, 
but also in the non-standard representations, size, and accessibility of data. 
To address this problem, we generated a new database of compounds, AqSolDB, by merging a total of nine different aqueous solubility datasets, 
curating the merged data, standardizing and validating the compound representation formats, marking with reliability labels, and providing 2D descriptors of compounds as a Supplementary Resource.
SN  - 2052-4463
UR  - https://doi.org/10.1038/s41597-019-0151-1
DO  - 10.1038/s41597-019-0151-1
ID  - Sorkun2019
ER  - 
```