| Name | qsarify JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | |
| Summary | QSARify: A tool for QSAR model development |
| upload_time | 2023-10-25 11:44:26 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | |
| license | |
| keywords |
qsar
cheminformatics
machine learning
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# qsarify
qsarify is a library of tools for the analysis of QSAR/QSPR datasets and models. This library is intended to be used to produce models which relate a set of calculated chemical descriptors to a given numeric endpoint. Many great tools will take the geometry or string data of a given chemical and compute **descriptors**, which are numeric measures of the properties of these, but you can generate some of these with another one of my scripts, [Free Descriptors](https://github.com/StephenSzwiec/free_descriptors).
# Dependencies
- Python 3
- [numpy](https://numpy.org/)
- [pandas](https://pandas.pydata.org/)
- [scikit-learn](https://scikit-learn.org)
- [matplotlib](https://matplotlib.org)
# Installation
`pip install qsarify`
# What is included right now?
- Data preprocessing tools: `data_tools`
- Dimensionality reduction via clustering: `clustering`
- Feature selection:
- Single threaded: `feature_selection_single`
- Multi-threaded: `feature_selection_multi`
- Model Export and Visualization: `model_export`
- Cross Valiidation: `cross_validation`
# How to use
The best way to learn how to use this library is to look at the example notebook in the `examples` folder. This notebook will walk you through the workflow of using this library to build a QSAR model.
# Future Plans
- Massively parallel feature selection methods:
- CUDA acceleration
- MPI acceleration
- Include Shannon Entropy as a dimensionality reduction metric in clustering
- Embedded kernel methods
- More visualization tools
- More cross validation tools
- Feature selection tools for categorical data
# Contributing
If you would like to contribute to this project, please feel free to fork this repository and submit a pull request. Otherwise, you may also submit an issue. I will try to respond to issues as quickly as possible.
# License
This project is licensed under the GNU GPLv3 license. See the LICENSE file for more details.
# Citation
If you use this library in your work, please cite it as follows:
Szwiec, Stephen. (2023). qsarify: A high performance library for QSAR model development.
BibTex:
```
@misc{szwiec2023qsarify,
author = {Szwiec, Stephen},
title = {qsarify: A high performance library for QSAR model development},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/stephenszwiec/qsarify}},
}
```
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"description": "# qsarify\n\nqsarify is a library of tools for the analysis of QSAR/QSPR datasets and models. This library is intended to be used to produce models which relate a set of calculated chemical descriptors to a given numeric endpoint. Many great tools will take the geometry or string data of a given chemical and compute **descriptors**, which are numeric measures of the properties of these, but you can generate some of these with another one of my scripts, [Free Descriptors](https://github.com/StephenSzwiec/free_descriptors).\n\n# Dependencies\n\n- Python 3\n- [numpy](https://numpy.org/)\n- [pandas](https://pandas.pydata.org/)\n- [scikit-learn](https://scikit-learn.org)\n- [matplotlib](https://matplotlib.org)\n\n\n# Installation\n\n`pip install qsarify`\n\n# What is included right now?\n\n- Data preprocessing tools: `data_tools`\n- Dimensionality reduction via clustering: `clustering`\n- Feature selection:\n\t- Single threaded: `feature_selection_single`\n\t- Multi-threaded: `feature_selection_multi`\n- Model Export and Visualization: `model_export`\n- Cross Valiidation: `cross_validation`\n\n# How to use\n\nThe best way to learn how to use this library is to look at the example notebook in the `examples` folder. This notebook will walk you through the workflow of using this library to build a QSAR model.\n\n# Future Plans\n\n- Massively parallel feature selection methods:\n\t- CUDA acceleration\n\t- MPI acceleration\n- Include Shannon Entropy as a dimensionality reduction metric in clustering\n- Embedded kernel methods\n- More visualization tools\n- More cross validation tools\n- Feature selection tools for categorical data\n\n# Contributing\n\n\nIf you would like to contribute to this project, please feel free to fork this repository and submit a pull request. Otherwise, you may also submit an issue. I will try to respond to issues as quickly as possible.\n\n# License\n\n\nThis project is licensed under the GNU GPLv3 license. See the LICENSE file for more details.\n\n# Citation\n\nIf you use this library in your work, please cite it as follows:\n\nSzwiec, Stephen. (2023). qsarify: A high performance library for QSAR model development.\n\nBibTex:\n```\n@misc{szwiec2023qsarify,\n author = {Szwiec, Stephen},\n title = {qsarify: A high performance library for QSAR model development},\n year = {2023},\n publisher = {GitHub},\n journal = {GitHub repository},\n howpublished = {\\url{https://github.com/stephenszwiec/qsarify}},\n }\n```\n",
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