Metadata-Version: 2.4
Name: firecode
Version: 1.5.3
Summary: FIRECODE: Filtering Refiner and Embedder for Conformationally Dense Ensembles
Author-email: Nicolò Tampellini <nicolo.tampellini@yale.edu>
License-Expression: LGPL-3.0-or-later
License-File: LICENSE
Requires-Python: <3.13,>=3.12
Requires-Dist: ase
Requires-Dist: inquirerpy
Requires-Dist: matplotlib
Requires-Dist: networkx
Requires-Dist: numpy
Requires-Dist: prettytable
Requires-Dist: prism-pruner
Requires-Dist: psutil
Requires-Dist: rdkit>=2025.9.3
Requires-Dist: rich
Requires-Dist: scipy
Provides-Extra: aimnet2
Requires-Dist: aimnet[ase]; extra == 'aimnet2'
Provides-Extra: full
Requires-Dist: aimnet[ase]; extra == 'full'
Requires-Dist: fairchem-core; extra == 'full'
Provides-Extra: uma
Requires-Dist: fairchem-core; extra == 'uma'
Description-Content-Type: text/markdown


# FIRECODE - Filtering Refiner and Embedder for Conformationally Dense Ensembles

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<p align="center">

  <img src="docs/images/logo.png" alt="FIRECODE logo" class="center" width="500"/>

</p>

FIRECODE is a computational chemistry workflow driver for the generation, optimization and refinement of conformational ensembles, also implementing some transition state ultilities.

It implements flexible and customizable workflows for conformer generation (via [CREST](https://github.com/crest-lab/crest), [RDKit](https://github.com/rdkit/rdkit)), double-ended TS search ([NEB](https://ase-lib.org/ase/neb.html) via [ASE](https://github.com/rosswhitfield/ase), [ML-FSM](https://github.com/thegomeslab/ML-FSM)), and (constrained) ensemble optimization through popular calculators like [XTB](https://github.com/grimme-lab/xtb), [TBLITE](https://github.com/tblite/tblite), [ORCA](https://www.orcasoftware.de/tutorials_orca/), and Pytorch Neural Network models ([AIMNET2](https://github.com/isayevlab/AIMNet2), [UMA](https://huggingface.co/facebook/UMA)) via [ASE](https://github.com/rosswhitfield/ase).

Conformational pruning is performed with the now standalone [PRISM Pruner](https://github.com/ntampellini/prism_pruner).

As a legacy feature from [TSCoDe](https://github.com/ntampellini/TSCoDe), FIRECODE can also assemble non-covalent adducts from conformational ensembles (embedding) programmatically.

## Installation

The package is distributed via `pip`, and the use of [`uv`](https://docs.astral.sh/uv/) is highly recommended. The default installation is minimalistic, and torch/GPU support requires dedicated installs:

```python
uv pip install firecode           # XTB, TBLITE, ORCA
uv pip install firecode[aimnet2]  # + AIMNET2
uv pip install firecode[uma]      # + UMA/OMOL
uv pip install firecode[full]     # + AIMNET2, UMA/OMOL
```

More installation details in the documentation.

## Documentation
Additional documentation on how to install and use the program can be found on [readthedocs](https://firecode.readthedocs.io/en/latest/index.html).
