molll: Data Driven Estimation of Molecular Log-Likelihood using Fingerprint Key Counting

This software provides models for estimating the likelihood of a molecule belonging to a specific dataset based on simple fingerprint key counting. The models, AtomLL and MolLL, are designed for outlier detection and class membership assignment. They offer potential applications in molecular generation and optimization. PropLL is included and uses scikit kernel density estimates on RDKit-derived and user-selectable properties.

Installation

Installed tagged release from PyPI

pip install molecule_ll

or directly from the repository without cloning:

pip install git+https://github.com/EBjerrum/molll.git

If you want to tinker and contribute, then clone and install in editable mode:

git clone [email protected]:EBjerrum/molll.git # Or you fork on GitHub
cd molll
pip install -e .

Usage

The code works on lists of RDKit Mol objects:

from molll import MolLL
molll = MolLL()
molll.analyze_dataset(mols_list)
molll.calculate_lls(other_or_same_mols)
#Or a single Mol object
molll.calculate_ll(single_mols)

Saving and loading from a text-based format:

molll.save("MySaveFile.json")

molll_clone = MolLL()
molll_clone.load("MySaveFile.json")

For convenience, some classes with precomputed data are available, currently based on LibInvent train data:

from molll import LibInventMolLLr1
molll = LibInventMolLLr1()
molll.calculate_lls(mols_list)

Additional Reading

There's a preprint on ChemRxiv with some example usages: https://doi.org/10.26434/chemrxiv-2024-hzddj