abcount 0.2.0

Extended cheminformatics package to work with acidic and basic groups in molecules.

Introduction

ABCount is an extended cheminformatics package to work with acidic and basic groups in molecules. The tool includes the following functionalities:

• ABCounter: SMARTS-based tool to determine the number of acidic and basic groups in molecules.
• ABClassBuilder: Builder class that accepts a dictionary of pKa numerical values and yields an ABClassData object with their corresponding classes such as STRONG, WEAK, and NONE.

How to install the tool

ABCount can be installed from pypi (https://pypi.org/project/abcount).

pip install abcount

Usage

ABCounter

from rdkit import Chem
from abcount import ABCounter

# Use the tool out of the box with default definitions.
mol = Chem.MolFromSmiles("[nH]1nnnc1-c3c2[nH]ncc2ccc3")
abc = ABCounter()
abc.count_acid_and_bases(mol)

{'acid': 2, 'base': 2}

from rdkit import Chem
from abcount import ABCounter

# Point the tool to using your own definitions.
# The format is JSON and attributes must be consistent to those in
# acid_definitions.json and base_definitions.json in abcount/data.
mol = Chem.MolFromSmiles("[nH]1nnnc1-c3c2[nH]ncc2ccc3")
abc = ABCounter(acid_defs_filepath="/my/path/acid_defs.json", base_defs_filepath="/my/path/base_defs.json")
abc.acid_matcher.definitions_fp

PosixPath('/my/path/acid_defs.json')

ABClassBuilder and ABClassData

from abcount import ABClassBuilder

abcb = ABClassBuilder()
# The builder expects two acidic and two basic groups with these key names.
predictions = {"pka_acid1": 3.5, "pka_acid2": None, "pka_base1": 9.785, "pka_base2": None}
abcb.build(predictions)

ABClassData(acid_1_class=<AcidType.STRONG: 'strong_acid'>, acid_2_class=<AcidType.NONE: 'no_acid'>, base_1_class=<BaseType.STRONG: 'strong_base'>, base_2_class=<BaseType.NONE: 'no_base'>)

from abcount import ABClassBuilder, PKaClassBuilder

abcb = ABClassBuilder()
# Custom names can be passed but these need to be configured in a `CustomPKaAttribute` class
predictions = {"my_pka_acid1": 3.5, "my_pka_acid2": None, "my_pka_base1": 9.785, "my_pka_base2": None}
CustomPKaAttribute = PKaClassBuilder.build(ACID_1="my_pka_acid1", BASE_1="my_pka_base1", ACID_2="my_pka_acid2", BASE_2="my_pka_base2")

# The `CustomPKaAttribute` can then be passed to the builder which will map the new data to the rules
abcb.build(predictions, CustomPKaAttribute)

ABClassData(acid_1_class=<AcidType.STRONG: 'strong_acid'>, acid_2_class=<AcidType.NONE: 'no_acid'>, base_1_class=<BaseType.STRONG: 'strong_base'>, base_2_class=<BaseType.NONE: 'no_base'>)

from abcount import ABClassBuilder, PKaClassBuilder

abcb = ABClassBuilder()
# It is possible to work with fewer acidic or basic groups
# These can be set as arguments in the builder
predictions = {"pka_acid1": 3.5, "pka_base1": 9.785}
abcb.build(predictions, num_acids=1, num_bases=1)

# Note that despite passing only one group per type, the builder still returns two groups each
ABClassData(acid_1_class=<AcidType.STRONG: 'strong_acid'>, acid_2_class=None, base_1_class=<BaseType.STRONG: 'strong_base'>, base_2_class=None)

SMARTS definitions source for ABCounter

The SMARTS patterns used in this project were obtained from the following sources. Note that definitions are not deduplicated, hence require curation to avoid redundant matching.

• Pan, X.; Wang, H.; Li, C.; Zhang, J. Z. H.; Ji, C., MolGpka: A Web Server for Small Molecule pKa Prediction Using a Graph-Convolutional Neural Network Journal of Chemical Information and Modeling 2021, 61 (7), 3159–3165. DOI: 10.1021/acs.jcim.1c00075
• Wu, J.; Wan, Y.; Wu, Z.; Zhang, S.; Cao, D.; Hsieh, C.-Y.; Hou, T., MF-SuP-pKa: Multi-fidelity modeling with subgraph pooling mechanism for pKa prediction Acta Pharmaceutica Sinica B 2023, 13 (6). DOI: 10.26434/chemrxiv-2022-t6q61
• Some manually curated definitions.

Some useful commands

• Generate acidic and basic definitions from aggregated data: python abcount/_definitions.py. A follow up on how definitions can be curated will be provided.
• Run tests: pytest -vss tests/test.py
• Run validation: cd tests && validation.py. This will also generate four CSV files listing out false positives and negatives for the test data.

For developers

• The package was created using uv (https://docs.astral.sh/uv/).
• The package can be installed from the wheel in the dist/ folder. When a new version needs to be released, a new wheel must be built. That can be done by changing the version of the package inside pyproject.toml then calling uv build which will create a new build.
• The code can be automatically tested using pytest -vss tests/test.py which requires pytest to be installed.
• The Makefile can also be used for building (make build) or testing (make test).
• Before committing new code, please always check that the style and syntax are compliant using pre-commit.

Setting up your development environment

The pyproject.toml already contains the optional dependencies needed for development. Follow these steps to set up the environment.

# Make sure you have got Python >= 3.10
python --version
> Python 3.12.7

# Installs `abcount` in editable mode and with dev dependencies
pip install -e .[dev]
> ...
> Successfully installed abcount ...

# Setup pre-commit hooks
pre-commit install
> pre-commit installed at .git/hooks/pre-commit