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 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 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).

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  -