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--- |
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version: 1.0.0 |
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language: en |
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license: mit |
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source_datasets: curated |
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task_categories: |
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- tabular-regression |
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tags: |
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- chemistry |
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- cheminformatics |
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pretty_name: Aqueous Solubility Database (AqSolDB) |
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dataset_summary: >- |
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AqsolDB contains solubility data for 9,982 unique compounds, curated from nine publicly available aqueous solubility datasets. |
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citation: >- |
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@article{ |
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author = {Murat Cihan Sorkun, Abhishek Khetan \& Süleyman Er}, |
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title = {AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds}, |
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journal = {Scientific Data}, |
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year = {2019}, |
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volume = {6}, |
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number = {143}, |
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month = {aug}, |
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url = {https://www.nature.com/articles/s41597-019-0151-1}, |
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publisher = {Springer Nature} |
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size_categories: |
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- 1K<n<10K |
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config_names: |
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- AqSolDB |
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configs: |
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- config_name: AqSolDB |
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data_files: |
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- split: test |
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path: AqSolDB/test.csv |
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- split: train |
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path: AqSolDB/train.csv |
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dataset_info: |
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- config_name: AqSolDB |
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features: |
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- name: "ID" |
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dtype: string |
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- name: "Name" |
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dtype: string |
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- name: "InChI" |
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dtype: string |
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- name: "InChIKey" |
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dtype: string |
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- name: "SMILES" |
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dtype: string |
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- name: "Y" |
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dtype: float64 |
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- name: "SD" |
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dtype: float64 |
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- name: "Ocurrences" |
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dtype: int64 |
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- name: "Group" |
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dtype: string |
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- name: "MolWt" |
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dtype: float64 |
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- name: "MolLogP" |
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dtype: float64 |
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- name: "MolMR" |
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dtype: float64 |
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- name: "HeavyAtomCount" |
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dtype: float64 |
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- name: "NumHAcceptors" |
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dtype: float64 |
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- name: "NumHDonors" |
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dtype: float64 |
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- name: "NumHeteroatoms" |
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dtype: float64 |
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- name: "NumRotatableBonds" |
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dtype: float64 |
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- name: "NumValenceElectrons" |
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dtype: float64 |
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- name: "NumAromaticRings" |
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dtype: float64 |
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- name: "NumSaturatedRings" |
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dtype: float64 |
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- name: "NumAliphaticRings" |
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dtype: float64 |
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- name: "RingCount" |
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dtype: float64 |
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- name: "TPSA" |
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dtype: float64 |
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- name: "LabuteASA" |
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dtype: float64 |
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- name: "BalabanJ" |
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dtype: float64 |
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- name: "BertzCT" |
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dtype: float64 |
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- name: "ClusterNo" |
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dtype: int64 |
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- name: "MolCount" |
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dtype: int64 |
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- name: "group" |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1737344 |
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num_examples: 7488 |
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- name: test |
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num_bytes: 578736 |
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num_examples: 2494 |
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--- |
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# Aqueous Solubility Database (AqSolDB) |
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|
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AqSolDB is created by the Autonomous Energy Materials Discovery [AMD] research group, consists of aqueous solubility values of |
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9,982 unique compounds curated from 9 different publicly available aqueous solubility datasets. This openly accessible dataset, |
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which is the largest of its kind, and will not only serve as a useful reference source of measured solubility data, but also |
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as a much improved and generalizable training data source for building data-driven models. |
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This is a mirror of the [official Github repo](https://github.com/mcsorkun/AqSolDB) where the dataset was uploaded in 2019. |
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## Quickstart Usage |
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|
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### Load a dataset in python |
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Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library. |
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First, from the command line install the `datasets` library |
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$ pip install datasets |
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then, from within python load the datasets library |
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|
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>>> import datasets |
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and load one of the `AqSolDB` datasets, e.g., |
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>>> AqSolDB = datasets.load_dataset("maomlab/AqSolDB", name = "AqSolDB") |
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Downloading readme: 100%|████████████████████| 10.2k/10.2k [00:00<00:00, 4.41MB/s] |
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Downloading data: 100%|█████████████████████████| 972k/972k [00:02<00:00, 432kB/s] |
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Downloading data: 100%|██████████████████████| 2.88M/2.88M [00:01<00:00, 1.92MB/s] |
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Generating test split: 100%|████████| 2494/2494 [00:00<00:00, 44727.48 examples/s] |
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Generating train split: 100%|██████| 7488/7488 [00:00<00:00, 144316.82 examples/s] |
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and inspecting the loaded dataset |
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|
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>>> AqSolDB |
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AqSolDB |
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DatasetDict({ |
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test: Dataset({ |
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features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Y', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\ |
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ectrons', 'NumAromaticRings', 'NumSaturatedRings', 'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ', 'BertzCT', 'ClusterNo', 'MolCount', 'group'], |
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num_rows: 2494 |
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}) |
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train: Dataset({ |
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features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Y', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\ |
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ectrons', 'NumAromaticRings', 'NumSaturatedRings', 'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ', 'BertzCT', 'ClusterNo', 'MolCount', 'group'], |
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num_rows: 7488 |
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}) |
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}) |
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### Use a dataset to train a model |
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One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia. |
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First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support |
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pip install 'molflux[catboost,rdkit]' |
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then load, featurize, split, fit, and evaluate the catboost model |
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import json |
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from datasets import load_dataset |
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from molflux.datasets import featurise_dataset |
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from molflux.features import load_from_dicts as load_representations_from_dicts |
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from molflux.splits import load_from_dict as load_split_from_dict |
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from molflux.modelzoo import load_from_dict as load_model_from_dict |
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from molflux.metrics import load_suite |
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split_dataset = load_dataset('maomlab/AqSolDB') |
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split_featurised_dataset = featurise_dataset( |
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split_dataset, |
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column = "SMILES", |
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representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}])) |
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model = load_model_from_dict({ |
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"name": "cat_boost_regressor", |
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"config": { |
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"x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'], |
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"y_features": ['Y']}}) |
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model.train(split_featurised_dataset["train"]) |
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preds = model.predict(split_featurised_dataset["test"]) |
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regression_suite = load_suite("regression") |
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scores = regression_suite.compute( |
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references=split_featurised_dataset["test"]['Y'], |
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predictions=preds["cat_boost_regressor::Y"]) |
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## Aqueous Solubility Database |
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### Data splits |
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The original AqSoDB dataset does not define splits, so here we have used the `Realistic Split` method described |
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in [(Martin et al., 2018)](https://doi.org/10.1021/acs.jcim.7b00166). |
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### Citation |
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TY - JOUR |
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AU - Sorkun, Murat Cihan |
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AU - Khetan, Abhishek |
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AU - Er, Süleyman |
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PY - 2019 |
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DA - 2019/08/08 |
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TI - AqSolDB, a curated reference set of aqueous solubility and 2D descriptors for a diverse set of compounds |
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JO - Scientific Data |
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SP - 143 |
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VL - 6 |
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IS - 1 |
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AB - Water is a ubiquitous solvent in chemistry and life. |
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It is therefore no surprise that the aqueous solubility of compounds has a key role in various domains, |
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including but not limited to drug discovery, paint, coating, and battery materials design. |
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Measurement and prediction of aqueous solubility is a complex and prevailing challenge in chemistry. |
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For the latter, different data-driven prediction models have recently been developed to augment the physics-based modeling approaches. |
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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. |
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Existing solubility datasets show variance in the chemical space of compounds covered, measurement methods, experimental conditions, |
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but also in the non-standard representations, size, and accessibility of data. |
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To address this problem, we generated a new database of compounds, AqSolDB, by merging a total of nine different aqueous solubility datasets, |
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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. |
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SN - 2052-4463 |
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UR - https://doi.org/10.1038/s41597-019-0151-1 |
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DO - 10.1038/s41597-019-0151-1 |
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ID - Sorkun2019 |
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ER - |
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``` |
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