Welcome to the installation guide. First off, AugLiChem is a python3.8+ package and requires pytorch>=1.10.0. Guides for installation in Linux (Ubuntu) and MacOS ARM64 (M1 chip) architecture are given. Installation for Linux is straightforward, but for MacOS ARM64, installation is more involved due to dependencies not yet being supported for the architecture.

Linux

It is recommended to use an environment manager such as conda to install AugLiChem. Instructions can be found here. If using conda, creating a new environment is ideal and can be done simply by running the following command:

conda create -n auglichem python=3.8

Then activating the new environment with

conda activate auglichem

AugLiChem is built primarily with pytorch and that should be installed independently according to your system specifications. After activating your conda environment, pytorch can be installed easily and instructions are found here.

torch_geometric needs to be installed with conda install pyg -c pyg -c conda-forge.

Once you have pytorch and torch_geometric installed, installing AugLiChem can be done using PyPI:

pip install auglichem

MacOS ARM64 Architecture

A more involved install is required to run on the new M1 chips since some of the packages do not have official support yet. We are working on a more elegant solution given the current limitations.

First, download this repo.

If you do not have it yet,, conda for ARM64 architecture needs to be installed. This can be done with Miniforge (which contains conda installer) which is installed by following the guide here

Once you have miniforge compatible with ARM64 architecture, a new environment with rdkit can be installed. If you do not specify python=3.8 it will default to python=3.9.6 as of the time of writing this.

conda create -n auglichem python=3.8 rdkit

Now activate the environment:

conda activate auglichem

From here, individual packages can be installed:

conda install -c pytorch pytorch

conda install -c fastchan torchvision

conda install scipy

conda install cython

conda install scikit-learn

pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cpu.html

pip install torch-sparse -f https://data.pyg.org/whl/torch-1.10.0+cpu.html

pip install torch-geometric

Before installing the package, you must go into setup.py in the main directory and comment out rdkit-pypi and tensorboard from the install_requires list since they are already installed. Not commenting these packages out will result in an error during installation.

Finally, run:

pip install .