semvec/pypi-packages huggingface dataset:
besmarts 0.1.0
Binary Encoded SMARTS
License: MIT
Requires Python: >=3.6


# BESMARTS

*A toolkit for data-driven force field design based on binary-encoded SMARTS*

Highlights of this package are:

* Map and perform bitwise operations between two molecular substructures of
  arbitrary size
* Search/iterate a substructure at the SMARTS-primitive level, using both
  _numerical_ and _analytic_ approaches
* Cluster molecular data by SMARTS using a SMARTS hierarchy
* Calculate energy and gradients using a classical force field based on (a
  basic implementation of) the SMIRNOFF format
* Geometry optimization
* Force field parameter optimization (under development)

See the ChemRxiv [preprint](https://doi.org/10.26434/chemrxiv-2023-v969f-v3)
for the theoretical unpinnings on which this package is based.

# Installation

Currently, the best way to install is to clone and then install with pip.

For environment users (e.g. venv or conda), one should probably create an empty
environment first:
```
conda create -n besmarts python
conda activate besmarts
```
or
```
python -m venv besmarts
. besmarts/bin/activate

```

followed by the actual install:

```
git clone https://github.com/trevorgokey/besmarts besmarts-git
cd besmarts-git/besmarts-core/python
python -m pip install .
cd ../../besmarts-rdkit/python
python -m pip install .
```

RDKit is needed to decode SMILES into graphs and offers a faster implementation
of SMARTS matching when labeling from a SMARTS hierarchy.

Geometry optimization uses the SciPy minimizer and can be installed using
using a similar process as above with `besmarts-scipy`. There is also an
interface to OpenMM and this minimizer can be used instead after installing
`besmarts-openmm`. The OpenMM plugin is quite a bit faster and is recommended
if large or heavily numerical computations are needed (e.g. fitting frequencies
from calculating MM Hessians with finite differences). The native interface
to calculating energies and gradients is useful if novel functional forms are
needed and not in standard packages (e.g. OpenMM). We recommend using OpenMM if
it is supported and available on your system. The energies, hessians, and
gradients compared between the native and OpenMM implementations are nearly
exact; for energy/gradient down to 12 decimal places for linear terms, around 6
places for torsions, and between 4-12 places for Hessians.

Molecular mechanics energy and gradient evaluations are implemented, but
require partial charges. By default, `besmarts` will try to charge molecules
with `am1bcc` using the `sqm` program from `ambertools` suite. Consequently,
make sure `sqm` is in your `PATH` by installing via `conda` or by other means.

# Documentation

Documentation in this repository is hosted on [RTD](https://besmarts.readthedocs.io)

# Contributing

Contributions in the form of bug reports, enhancements, and general discussions
are very welcome. See
[CONTRIBUTING.md](https://github.com/trevorgokey/besmarts/blob/main/CONTRIBUTING.md) for
more details.