| Name | polypharm JSON |
| Version |
0.4.3
JSON |
| download |
| home_page | None |
| Summary | A Python-based library to perform IFD and MMGBSA calculations on different targets using a polypharmacological approach. |
| upload_time | 2024-05-06 21:37:11 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2023 Mauricio Bedoya Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
computational-chemistry
polypharmacology
molecular-docking
induced-fit
free-energy-calculations
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Polypharm
[<img align="left" src="./polypharm/assets/polypharm_logo_transparent_001.png" width="250" />](./polypharm/assets/polypharm_logo_transparent_001.png) A Python-based library to perform induced fit docking (IFD) and MM/GBSA
calculations on different targets using a polypharmacological approach.
Refer to the [official documentation](http://polypharm.rtfd.io/) for
details about installation, usage, methodology, and developer interface.
## Installation
The version at the Python Package Index (PyPI) is always the latest
stable release that is relatively bug-free and can be installed via pip:
```shell
pip install polypharm
```
The minimum Python version is 3.9, and requires the pandas and Jinja2
packages. Refer to the
[Installation](http://polypharm.rtfd.io/en/latest/installation.html)
page of the documentation for more details.
**NOTE**: The main functionality (i.e., IFD and MM/GBSA) does require a
working [Schrödinger Suite](https://schrodinger.com) installation (2018-4 or
greater) including the Glide and Prime modules.
## Usage
`polypharm` can be used either programmatically or from the command
line. There is Jupyter Notebook at the examples folder that shows the
common usage. In any case, below is a brief example.
```python
import glob
import os
import polypharm as ppm
# required to run docking and binding energy calculation
os.environ["SCHRODINGER_PATH"] = "/path/to/schrodinger"
# gather input files and configuration
prot_files = glob.glob(os.path.join("proteins", "*.mae*"))
lig_files = glob.glob(os.path.join("molecules", "*.mae*"))
resid_map = {
"6lqa": ["B:1462", "B:1466", "B:1760", "B:1767"],
"6rv3": [
"A:126", "A:171", "A:194", "A:198", "A:199", "A:234", "A:235",
"A:236", "A:238"
],
}
parallel = 10
# 1. Run induced-fit cross docking
ppm.cross_dock(prot_files, lig_files, resid_map, tasks=parallel)
# 2. Rescore generated IFD poses using MM/GBSA
ppm.rescore_docking(
glob.glob(os.path.join("ifd", "**", "*", "*-out.maegz")),
tasks=parallel,
)
# 3. Generate a report from MM/GBSA output
maefiles = glob.glob(os.path.join("mmgbsa", "**", "*-out.maegz"))
results = ppm.report(maefiles, resid_map, tasks=parallel)
# 4. Rank molecules by their docking performance
ranked_results = ppm.rank_molecules(results)
```
The same workflow can be performed via command line:
```shell
$ vim resids.txt # write residues into a file
$ mkdir ifd && cd ifd
$ python -m polypharm dock -p ../proteins -r ../resids.txt -t 5 ../ligands
$ cd ..
$ mkdir mmgbsa && cd mmgbsa
$ python -m polypharm rescore -t 5 ../ifd
$ cd ..
$ python -m polypharm report -o report.csv -c 6 -t 5 mmgbsa
$ python -m polypharm rank -o rank.csv report.csv
```
Please refer to the [official documentation](http://polypharm.rtfd.io)
for more information.
## Citing
If you use `polypharm` in your research, please consider citing the
following article:
To be added
## Contributors
- [Mauricio Bedoya](https://github.com/maurobedoya) - creator,
maintainer
- [Francisco Adasme](https://github.com/franciscoadasme) - maintainer
## License
Licensed under the MIT license, see the separate LICENSE file.
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"description": "# Polypharm\n\n[<img align=\"left\" src=\"./polypharm/assets/polypharm_logo_transparent_001.png\" width=\"250\" />](./polypharm/assets/polypharm_logo_transparent_001.png) A Python-based library to perform induced fit docking (IFD) and MM/GBSA\ncalculations on different targets using a polypharmacological approach.\n\nRefer to the [official documentation](http://polypharm.rtfd.io/) for\ndetails about installation, usage, methodology, and developer interface.\n\n## Installation\n\nThe version at the Python Package Index (PyPI) is always the latest\nstable release that is relatively bug-free and can be installed via pip:\n\n```shell\npip install polypharm\n```\n\nThe minimum Python version is 3.9, and requires the pandas and Jinja2\npackages. Refer to the\n[Installation](http://polypharm.rtfd.io/en/latest/installation.html)\npage of the documentation for more details.\n\n**NOTE**: The main functionality (i.e., IFD and MM/GBSA) does require a\nworking [Schr\u00f6dinger Suite](https://schrodinger.com) installation (2018-4 or\ngreater) including the Glide and Prime modules.\n\n## Usage\n\n`polypharm` can be used either programmatically or from the command\nline. There is Jupyter Notebook at the examples folder that shows the\ncommon usage. In any case, below is a brief example.\n\n```python\nimport glob\nimport os\n\nimport polypharm as ppm\n\n# required to run docking and binding energy calculation\nos.environ[\"SCHRODINGER_PATH\"] = \"/path/to/schrodinger\"\n\n# gather input files and configuration\nprot_files = glob.glob(os.path.join(\"proteins\", \"*.mae*\"))\nlig_files = glob.glob(os.path.join(\"molecules\", \"*.mae*\"))\nresid_map = {\n \"6lqa\": [\"B:1462\", \"B:1466\", \"B:1760\", \"B:1767\"],\n \"6rv3\": [\n \"A:126\", \"A:171\", \"A:194\", \"A:198\", \"A:199\", \"A:234\", \"A:235\",\n \"A:236\", \"A:238\"\n ],\n}\nparallel = 10\n\n# 1. Run induced-fit cross docking\nppm.cross_dock(prot_files, lig_files, resid_map, tasks=parallel)\n# 2. Rescore generated IFD poses using MM/GBSA\nppm.rescore_docking(\n glob.glob(os.path.join(\"ifd\", \"**\", \"*\", \"*-out.maegz\")),\n tasks=parallel,\n)\n# 3. Generate a report from MM/GBSA output\nmaefiles = glob.glob(os.path.join(\"mmgbsa\", \"**\", \"*-out.maegz\"))\nresults = ppm.report(maefiles, resid_map, tasks=parallel)\n# 4. Rank molecules by their docking performance\nranked_results = ppm.rank_molecules(results)\n```\n\nThe same workflow can be performed via command line:\n\n```shell\n$ vim resids.txt # write residues into a file\n$ mkdir ifd && cd ifd\n$ python -m polypharm dock -p ../proteins -r ../resids.txt -t 5 ../ligands\n$ cd ..\n$ mkdir mmgbsa && cd mmgbsa\n$ python -m polypharm rescore -t 5 ../ifd\n$ cd ..\n$ python -m polypharm report -o report.csv -c 6 -t 5 mmgbsa\n$ python -m polypharm rank -o rank.csv report.csv\n```\n\nPlease refer to the [official documentation](http://polypharm.rtfd.io)\nfor more information.\n\n## Citing\n\nIf you use `polypharm` in your research, please consider citing the\nfollowing article:\n\n To be added\n\n## Contributors\n\n- [Mauricio Bedoya](https://github.com/maurobedoya) - creator,\n maintainer\n- [Francisco Adasme](https://github.com/franciscoadasme) - maintainer\n\n## License\n\nLicensed under the MIT license, see the separate LICENSE file.\n",
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