This repository is related to the paper "Electron Density-Based GPT for Optimization and Suggestion of Host-Guest Binders". Please check the manuscript, and especially the Supplementary Information document, for full information about how to run the model, and about the results we obtained.
What follows is a very brief guide about how to get the repository started.
We recommend running Electrondensity in conda environment. The following command creates conda environment with necessary dependencies.
conda env create -f environment.yaml
File env.sh contains environmental variables that can be modified to suit your configuration:
DATA_DIR is the path for storing datasets LOG_DIR is the path for storing log files MODEL_DIR is a path for storing and saving models CPU_COUNT specifies number of cpus used for parallel processing of datasets default -1 means all available CPUs.
It also downloads and installs orbkit package which cannot be installed using conda. To finish installation and activate electrondensity environmet run:
source env.shpython bin/generate_dataset.py QM9This command will download and generate electron densities for QM9 dataset (it requires 256GB od harddisk space) using number of CPUs specified in CPU_COUNT.
Please check the Supplementary Information document for full documentation about how to install, train and run the different models.