semvec/pypi-packages huggingface dataset:
dgbg 0.0.2
Docking grid box generator for Autodock4 and Vina
License: MIT
Keywords: core package

# Docking Grid Box Generator (DGBG)

Python module to automatically generate a grid box around a ligand present in a pdb file.

## Preview

![screenshot](result.gif)

## Table of contents

- [Demo-Preview](#demo-preview)
- [Table of contents](#table-of-contents)
- [Installation](#installation)
- [Usage](#usage)
- [Development](#development)
- [Acknowledgements](#acknowledgements)
- [License](#license)


## Installation
[(Back to top)](#table-of-contents)

To use this project, pip install the module on your device using the command below:

```pip install dgbg```

## Usage
[(Back to top)](#table-of-contents)

```python
from dgbg import dgbg

box = dgbg.binding_box('ex_file_path.pdb')

ligand_data = box.get_ligand_data('ligand_ID', 'chain_ID')

centroid, box_size = box.create_box(spacing=0.375, padding=np.array([0, 0, 0]))

# Visualize using the following commands in Jupyter Notebook.

result = box.show_result(centroid, box_size)

result

```

## Examples
[(Back to top)](#table-of-contents)

1. General example use case.

2. Adjusting grid box after generation.

3. Integration with DeepChem and its docking functionality.

4. Integration with AutoDock Vina.

5. Multiple identical ligands on the same protein chain.

## Development
[(Back to top)](#table-of-contents)

1. Git Clone Repository
```git clone git@github.com:ujamshed/dgbg.git```

2. Create Virtual Environment
```python3  -m venv .venv```

3. Activate Virtual Environment
```source .venv/bin/activate```

4. Install Project Dependencies
```pip install -r requirements.txt```

## Acknowledgements

This software uses the following open source packages:

- [NumPy](https://numpy.org/)
- [Pandas](https://pandas.pydata.org/)
- [MDTraj](https://www.mdtraj.org/1.9.8.dev0/index.html)
- [nglview](https://github.com/nglviewer/nglview)

## License
[(Back to top)](#table-of-contents)

[MIT](https://opensource.org/licenses/MIT)
memory-efficient-transformer-pytorch 0.2.0
Implementation on PyTorch of Self-attention Does Not Need $O(n^2)$ Memory
License: MIT
Requires Python: >=3.7,<3.11

# faster-transformer

*Self-attention Does Not Need $O(n^2)$ Memory*のPytorch実装

```bibtex
@misc{rabe2021selfattention,
    title={Self-attention Does Not Need $O(n^2)$ Memory}, 
    author={Markus N. Rabe and Charles Staats},
    year={2021},
    eprint={2112.05682},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```