https://github.com/SEDenmarkLab/molli
- Alexander S. Shved
- Blake E. Ocampo
- Elena S. Burlova
- Casey L. Olen
- N. Ian Rinehart
S. E. Denmark Laboratory, University of Illinois, Urbana-Champaign
For all work using molli, please cite the primary publication: Shved, A. S.; Ocampo, B. E.; Burlova, E. S.; Olen, C. L.; Rinehart, N. I.; Denmark. S. E.; J. Chem. Inf. Mod. 2024, DOI: 10.1021/acs.jcim.4c00424
Docuementation and Example Workflows can be found here.
Supplementary Materials and Libraries can be found on the Zenodo Repository: DOI: 10.5281/zenodo.10719790
Copyright 2022-2023 The Board of Trustees of the University of Illinois. All Rights Reserved.
Molli is a cross-platform toolbox written in modern Python (3.10+) that provides a convenient API for molecule manipulations, combinatorial library generation with stereochemical fidelity from plain CDXML files, as well as parallel computing interface. The main feature of molli is the full representation of molecular graphs, geometries and geometry ensembles with no implicit atoms. Additionally, a compact and extensible format for molecular library storage make it a useful tool for in silico library generation. molli is cross-platform code that runs on a wide range of hardware from laptops and workstations to distributed memory clusters.
Molli is available as the source code distribution on GitHub. Additionally, convenient installation is provided in the form of a PyPi package and conda package.
Note: We routinely test the package on Linux and Windows OS. OSX support is tested upon the pull request submission using GitHub workflows. We can only offer limited support for that OS at this time.
The easiest way to obtain molli is to obtain the latest PyPI package.
pip install molliUpon a successful installation of molli, one can test the installation by running the following commands
molli --VERSION\
molli test -vvwhich will provide the current version (it is obtained dynamically from the Git tags and determined at the installation time) and run the full test suite to guarantee that the core functionality performs correctly.
Installation from source can offer a few advantages, such as the editable installation, or installing . This is convenient for users who wish to significantly alter their molli experience by modifying the core functionality
pip install git+https://github.com/SEDenmarkLab/molli.git
# or
pip install -e git+https://github.com/SEDenmarkLab/molli.git#egg=molliEditable installation: (Assumes that the repository source code was cloned onto the hard drive using Github tools into ./molli/ folder) We have noticed that development with VSCode is not greatly compatible with the most recent version of the
pip install -e molli/ --config-settings editable_mode=compatMolli can be installed from a conda repository:
Note: Conda setup is not fully configured yet, so you may expect that there will be slight changes to the syntax. For more information about the current installation instructions, please visit the Anaconda repository
conda install esalx::molliNote: By default, molli only tests the core functionality: the functions that do not depend on external computational or chemoinformatics packages, such as OpenBabel, RDKit, Orca, XTB and CREST. These tests are considered extended (see below).
There are two syntaxes that allow to test the functionality of molli, of which one (molli test) is a more convenient alias for another (python -m unittest). See unittest documentation for additional arguments, which can be applied to both.
python -m unittest molli_test # additional args
molli test # additional argsThese tests are automatically invoked if the corresponding packages are either importable (that is, installed in the same conda environment or the corresponding Python virtual environment), or the corresponding executables can be located. An example of such extended test is found below (if Environment Modules configures the packages on your machine)
module load xtb/6.4.1
module load crest/2.11.1
molli test -vv # Now this tests XTB and CREST driver