This is a python package to support Chemical property prediction using PyTorch and PyTorch Geometric. The ambition of this project is to build a flexible pipeline that lets users go from molecule descriptors (SMILES) to a fully functioning Graph Neural Network and allow for useful customization at every step.
For more information, please check out the docs.
To use the package, please run the following inside a terminal:
pip install grape-chem
After installing, the package will work like any other. See Demo
and Advanced Demo inside of docs
for an introduction of how the toolbox can be used.
If optimization is run on hpc using GraPE and the optimization procedure outlined in
the Advanced Demonstration, the following requirements need to be met:
python==3.9
cuda==12.1
and the following package need to be re-installed using the correct cuda-version:
torch==2.1.2
dgl~=1.1.3
torch-scatter -f https://data.pyg.org/whl/torch-2.1.2+cu121.html
ray
ConfigSpace==0.4.18
hpbandster==0.7.4
The reason for the particular python version is a subpackage in hpbandster.