FFPOPT Quick Start Guide - Installation

Zeke A. Piskulich1, and Darrin M. York1
1Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA

Learning objectives

  • Learn how to install the FFPOPT package and its dependencies

Relevant literature

  • Coming Soon!

Tutorial

In this tutorial, we will be using a small molecular fragment that contains a deprotonated amide. These fragments can show up in certain ligands, and are presently a challenge for standard force field generation within amber. We will walk through how to use FFPOPT to scan dihedral angles with both MD force-fields and machine learning potentials, and then we will walk through the process of fitting new dihedral parameters.

Installation

To run this tutorial, you will need to have FFPOPT installed. You can find the installation instructions in the FFPOPT GitHub repository: FFPOPT GitHub <https://github.com/tjgiese/ffpopt>.

Installation instructions may be found in the readme file. You will also need to have AMBER installed, as well as Python 3.7+ with the numpy and scipy libraries.

Important

Amarel Users - Good News! FFPOPT is already installed on Amarel. You can load it using the following command:

module purge
module use /projectsp/f_lbsr/YorkGroup/software/23jun25/modulefiles
# One of these two lines, depending on the ML potential you want to use
#module load 23jun25/ffpopt/21aug25.g-pytorch
#module load 23jun25/ffpopt/21aug25.g-psi4

Note

If you are using actual PSI4 for quantum calculations, this introduces incompatibilities with other packages. It is recommended to use the psi4 version of FFPOPT for this tutorial. These instructions are included in the ReadMe.

If you are running this tutorial on your local machine, you can install FFPOPT and its dependencies using the following commands:

# Local Installation example
git clone https://github.com/tjgiese/ffpopt.git
cd ffpopt
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
bash ./Miniforge3-Linux-x86_64.sh -b -f -p ${PWD}/miniforge3
source ${PWD}/miniforge3/bin/activate
conda install -y dpdata deepmd-kit ase parmed dacase::ambertools-dac=25 dftd3-python rdkit
python3 -m pip install torch torchvision torchaudio
python3 -m pip install cmake tblite mace-torch geometric aimnet torchani
conda deactivate
source ${PWD}/miniforge3/bin/activate
cd build
bash ./run_cmake.sh