This C++/Python library is a chemical space exploration software. It is based on the Molpher program which introduced a chemical space exploration method called molecular morphing. The original Molpher method uses stochastic optimization to traverse chemical space between two existing molecules. The main promise of this algorithm is that a virtual library enriched in compounds with improved biological activity could be generated in this way. The library is based on the popular RDKit cheminformatics framework, which makes work with the generated structures very easy.

The purpose of Molpher-lib is to bring molecular morphing closer to the cheminformatics community, but also offer new features that go beyond the capabilities of the original Molpher program. Molpher-lib makes it possible to roam the chemical universe freely and with little constraints on the inputs. For example, we could just use a carbon atom as a starting point and have Molpher-lib autonomously evolve it into a complete molecular structure. To ensure that the generated molecules have required properties, Molpher-lib also helps with implementation of custom rules and constraints. If you want to know more about Molpher-lib and its usage, make sure to check out some examples on the website. We also have some Jupyter notebooks with examples that you can explore.

If you would like to participate in the development or just check out the current features of the library, there is extensive documentation which can help you. A big part of the documentation is dedicated to a detailed tutorial that should introduce the philosophy of Molpher-lib in more detail and give you a good idea of what it is currently capable of.

The library is actively developed and many new features are planned to be added. The long-term goal is to make Molpher-lib a universal and easy-to-use de novo drug design framework. Ideas, comments and feature requests are more than welcome and can be submitted to the issue tracker. You can also subscribe to the RSS feed of the dev branch for development updates. If you want to know what is new in the current version, you can look at the changelog.

Supported platforms:

• Linux 64-bit

At the moment, the library binaries are only compiled for 64-bit Linux systems. However, development for other platforms is also planned. If you manage to compile the library on a different platform, consider making a pull request or comment on the issue tracker. Any help is much appreciated.

Molpher-lib is distributed as a conda package. At the moment, this is the preferred way to install and use the library. All you need to do is get the full Anaconda distribution or its lightweight variant, Miniconda. It is essentially a Python distribution, package manager and virtual environment in one and makes setting up a development environment for any project very easy. After installing Anaconda/Miniconda you can run the following in the Linux terminal:

conda install -c conda-forge -c lich molpher-lib

This will automatically download the latest version of the library and install everything to the currently active environment (for more information on environments and the conda command see Conda Test Drive). The library depends on a few other packages so do not forget to add the conda-forge channel.

If you are interested in the development snapshots of the library (most up to date code, but can contain bugs) , use the dev channel instead:

conda install -c conda-forge -c lich/label/dev molpher-lib

After that the library should be importable in Python and you should be able to successfully run the integrated unit tests:

from molpher.tests import run

run()

For a quick start, you can check out the Jupyter notebooks that contain examples from the documentation.

Compiling and installing from source is a little bit more elaborate. This process is described in detail in the documentation, but in the simplest case you can use the attached conda build environment to get all the dependencies and tools you will need for compilation:

# clone the repository
git clone https://github.com/lich-uct/molpher-lib.git
export REPOSITORY_ROOT=`pwd`/molpher-lib

# create the build environment from the attached file and activate it
cd ${REPOSITORY_ROOT}
git checkout dev # or the branch/tag/commit you want
conda env create -n {name} -f environment.yml # replace {name} with the name of your environment, i.e. molpher-lib-build
conda activate {name}

# build the library itself
mkdir cmake-build
cd cmake-build
cmake .. -DCMAKE_BUILD_TYPE=Debug -DPYTHON_EXECUTABLE=python3 -DRUN_SWIG=ON # you can specify other options (see the documentation link above for more info)
make molpher_install_python # compile the library and Python wrappers, will also install to ${REPOSITORY_ROOT}/dist

With the build environment still active, set the appropriate variables:

cd ${REPOSITORY_ROOT}
export PYTHONPATH=${REPOSITORY_ROOT}/dist/lib/python3.9/site-packages/molpher-0.0.0b3-py3.9-linux-x86_64.egg/ # change according to your Python and Molpher-lib version
export LD_LIBRARY_PATH=${REPOSITORY_ROOT}/dist/lib/:${CONDA_PREFIX}/lib/

and you should be able to import the built library from Python. You can verify the installation by running unit tests:

python3

Inside the interpreter:

from molpher.tests import run

run()

If you want to explore some example code from the documentations, there are a few Jupyter notebooks located under doc/notebooks. Just install Jupyter to your build environment and set the variables as above and you can simply run the notebookes like so:

cd ${REPOSITORY_ROOT}/doc/notebooks/
jupyter-notebook

This installation process has been tested on common Debian-based systems so experience on other Linux flavors may differ. If you run into problems, report them to the issue tracker and hopefully someone will be able to help.