molmetrics 1.0.0

A package for calculating QED scores, molecular descriptors, and optimized geometry descriptors for libraries of small molecules.

molmetrics

A package for calculating QED scores, molecular descriptors, and optimized geometry descriptors for libraries of small molecules.

Installation

You can install the latest release of molmetrics from PyPI using pip:

pip install molmetrics

For the best experience, it is recommended to use a virtual environment (such as venv or conda).

Conda environment (recommended for reproducibility)

A sample conda environment file is provided at devtools/conda-envs/test_env.yaml. To create and activate the environment:

conda env create -f devtools/conda-envs/test_env.yaml
conda activate molmetrics-test

This will install all required dependencies for development and testing.

Command line options

    -h, --help            show this help message and exit
    -d DIRECTORY, --directory DIRECTORY
                        Path to a directory containing input files (.sdf, .csv, .xlsx, .pkl).
    -f FILE [FILE ...], --file FILE [FILE ...]
                        Path(s) to input file(s) (.sdf, .csv, .xlsx, .pkl). Example: -f file1.sdf file2.csv
    -o OUT, --out OUT     Path to output directory. (Default=directory of input file)
    -c COLUMN, --column COLUMN
                        Name/substring of column containing SMILES strings. (Default=SMILES)
    -s [SUBSTRUCTURES ...], --substructures [SUBSTRUCTURES ...]
                        Provide SMARTS substructure to remove before calculating properties. Use 'diazirine_handle' to
                        remove diazarne FFF handles. (Default=None)
    -p, --properties      Adds QED properties to outputs. (Default=False)
    -md, --moldescriptors
                        Adds selected molecular descriptors to outputs. (Default=False)
    -g, --geometry        Adds NPR1, NPR2, and geometry descriptor to outputs. NOTE: This feature is resource intensive,
                        using the --conformers argument to decrease the required resources. (Default=False)
    -rs RANDOM_SEED, --random_seed RANDOM_SEED
                        Random seed for ETKDGv3 conformer generation. (Default=1789)
    -ft FORCE_TOLERANCE, --force_tolerance FORCE_TOLERANCE
                        Optimizer force tolerance for ETKDGv3 conformer optimization. Use 0.0135 for faster
                        performance. (Default=0.001)
    -pt PRUNE_THRESH, --prune_thresh PRUNE_THRESH
                        RMSD (Å) threshold for filtering conformers during ETKDGv3 generation. Conformers below this
                        threshold are discarded to reduce redundancy. (Default=0.1)
    -cf NUM_CONFORMERS, --num_conformers NUM_CONFORMERS
                        Number of conformers to generate using ETKDGv3. (Default=1000)
    -er ENERGY_RANGE, --energy_range ENERGY_RANGE
                        Energy range in kcal/mol for Boltzmann averaging. (Default=3.0)
    -ni, --no_img         Include 3D molecule images from the output XLSX. (Default=False)

Output files

Molmetrics generates 4 output files.

    XXX_qed.xlsx        # Spreadsheet of all molecular properties calculated with 2D and 3D molecular visualizations.
    XXX_qed.html        # Spreadsheet of all molecular properties calculated with 2D and 3D molecular visualizations.
    XXX_conformers.pkl  # Saved RDKIT mol objects with embedded and optimized conformers.
    XXX_qed.sdf         # Optimized conformer of molecule with embedded molecular properties.

Copyright

Copyright (c) 2025, Seth D. Veenbaas