# Installation

There are several ways that BespokeFit and its dependencies can be installed, including using the conda package manager.

## Using conda

The recommended way to install openff-bespokefit is via the conda package manager. A working installation also requires at least one package from each of the two sections below (“Fragmentation Backends” and “Reference Data Generators”)

conda install -c conda-forge openff-bespokefit


### Fragmentation Backends

#### AmberTools Antechamber

AmberTools is free and open-source, and can generally be used fragment molecules up to 40 heavy atoms in under 10 minutes.

conda install -c conda-forge ambertools


#### OpenEye Toolkits

If you have access to the OpenEye toolkits (namely oechem, oequacpac and oeomega) we recommend installing these also as they can speed up certain operations significantly. OpenEye software requires a free-for-academics license to run.

conda install -c openeye openeye-toolkits


### Reference Data Generators

#### Psi4

Psi4 is an open source quantum chemistry package that enables BespokeFit to generate bespoke QC data, and is recommended to be installed unless you intend to train against data generated using a surrogate such as ANI:

conda install -c conda-forge -c defaults -c psi4 psi4


Warning

There is an incompatibility between the AmberTools and Psi4 conda packages on Mac, and it is not possible to create a working conda environment containing both.

#### XTB

The xtb package gives access to the XTB semi-empirical models produced by the Grimme group in Bonn which may be used as a much faster surrogate when generating QC reference data (see Quick start for more details):

conda install -c conda-forge xtb-python


#### TorchANI

TorchANI is a pytorch implementation of the ANI neural network potentials from the Roitberg group that can be used as a much faster surrogate when generating QC reference data (see Quick start for more details):

conda install -c conda-forge torchani


Note

TorchANI potentials are only suitable for molecules with a net neutral charge and have limited element coverage consisting of C, H, N, O, S, F and Cl

## From source

To install openff-bespokefit from source begin by cloning the repository from github,

git clone https://github.com/openforcefield/openff-bespokefit
cd openff-bespokefit


create a custom conda environment which contains the required dependencies and activate it,

conda env create --name openff-bespokefit --file devtools/conda-envs/test-env.yaml
conda activate openff-bespokefit


and finally install the package itself:

python setup.py develop