| Name | qsprpred JSON |
| Version |
3.1.1
JSON |
| download |
| home_page | None |
| Summary | A cheminformatics package for training and testing QSAR/QSPR models |
| upload_time | 2024-07-02 13:53:24 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2023 CDD 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 |
qsar
machine learning
cheminformatics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[**Main**](https://github.com/CDDLeiden/QSPRpred/) [](https://github.com/CDDLeiden/QSPRpred/actions/workflows/docs_ci.yml) [](https://github.com/CDDLeiden/QSPRpred/actions/workflows/dev_ci.yml)  
[**Dev**](https://github.com/CDDLeiden/QSPRpred/tree/dev) [](https://github.com/CDDLeiden/QSPRpred/actions/workflows/docs_ci.yml) [](https://github.com/CDDLeiden/QSPRpred/actions/workflows/dev_ci.yml) 
QSPRpred
====================
<img src='figures/QSPRpred_logo.jpg' width=10% align=right>
<p align=left width=70%>
QSPRpred is open-source software libary for building **Quantitative Structure Property
Relationship (QSPR)** model developed by Gerard van Westen's Computational Drug
Discovery group. It provides a unified interface for building QSPR models based on
different types of descriptors and machine learning algorithms. We developed this
package to support our research, recognizing the necessity to reduce repetition in our
model building workflow and improve the reproducibility and reusability of our models.
In making this package available here, we hope that it may be of use to other
researchers as well. QSPRpred is still in active development, and we welcome
contributions and feedback from the community.
QSPRpred is designed to be modular and extensible, so that new functionality can be
easily added. A command line interface is available for basic use cases to quickly,
explore varying scenarios. For more advanced use cases, the Python API offers extra
flexibility and control, allowing more complex workflows and additional features.
Internally, QSPRpred relies heavily on the <a href="https://www.rdkit.org">RDKit</a>
and <a href="https://scikit-learn.org/stable/">scikit-learn</a> libraries. Furthermore,
for scikit-learn model saving and loading, QSPRpred
uses <a href="https://github.com/OlivierBeq/ml2json">ml2json</a> for safer and
interpretable model serialization. QSPRpred is also interoperable
with <a href="https://github.com/OlivierBeq/Papyrus-scripts">Papyrus</a>, a large scale
curated dataset aimed at bioactivity predictions, for data collection. Models developed
with QSPRpred are compatible with the group's *de novo* drug design
package <a href="https://github.com/CDDLeiden/DrugEx/">DrugEx</a>.
Quick Start
===========
## Installation
QSPRpred can be installed with pip like so (with python >= 3.10):
```bash
pip install qsprpred
```
Note that this will install the basic dependencies, but not the optional dependencies.
If you want to use the optional dependencies, you can install the package with an
option:
```bash
pip install qsprpred[<option>]
```
The following options are available:
- extra : include extra dependencies for PCM models and extra descriptor sets from
packages other than RDKit
- deep : include deep learning models (torch and chemprop)
- chemprop: include the ChemProp integration (only ChemProp versions < 2.0.0 supported at the moment)
- full : include all optional dependecies (requires cupy, `pip install cupy-cudaX`,
replace X with your [cuda version](https://docs.cupy.dev/en/stable/install.html))
### Note on PCM Modelling
If you plan to optionally use QSPRpred to calculate protein descriptors for PCM, make
sure to also install Clustal Omega. You can get it via `conda` (**for Linux and MacOS
only**):
```bash
conda install -c bioconda clustalo
```
or install MAFFT instead:
```bash
conda install -c biocore mafft
```
This is needed to provide multiple sequence alignments for the PCM descriptors. If
Windows is your platform of choice, these tools will need to be installed manually or a
custom implementation of the `MSAProvider` class will have to be made.
## Use
After installation, you will have access to various command line features and you can
use the Python API directly (
see [Documentation](https://cddleiden.github.io/QSPRpred/docs/)). For a quick start, you
can also check out the [Jupyter notebook tutorials](./tutorials/README.md), which
document the use of the Python API to build different types of models. The tutorials as
well as the documentation are still work in progress, and we will be happy for any
contributions where it is still lacking.
Contributions
=============
Contributions and issue reports are more than welcome. Pull requests can be made
directly to the `main` branch and we will transfer them
to [contrib](https://github.com/CDDLeiden/QSPRpred/tree/contrib) when scheduled for the
next release.
Workflow
========
Current Development Team
========================
- [H. van den Maagdenberg](https://github.com/HellevdM)
- [M. Sicho](https://github.com/martin-sicho)
- [L. Schoenmaker](https://github.com/LindeSchoenmaker)
- [O. Béquignon](https://github.com/OlivierBeq)
- [S. Luukkonen](https://github.com/sohviluukkonen)
- [M. Gorosiola González](https://github.com/gorostiolam)
- [D. Araripe](https://github.com/David-Araripe)
- [C. Jespers](https://github.com/ChielJespers)
- [A. Bernatavicius](https://github.com/andriusbern)
- [R. van den Broek](https://github.com/rlvandenbroek)
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"description": "[**Main**](https://github.com/CDDLeiden/QSPRpred/) [](https://github.com/CDDLeiden/QSPRpred/actions/workflows/docs_ci.yml) [](https://github.com/CDDLeiden/QSPRpred/actions/workflows/dev_ci.yml)  \n\n[**Dev**](https://github.com/CDDLeiden/QSPRpred/tree/dev) [](https://github.com/CDDLeiden/QSPRpred/actions/workflows/docs_ci.yml) [](https://github.com/CDDLeiden/QSPRpred/actions/workflows/dev_ci.yml) \n\nQSPRpred\n====================\n\n<img src='figures/QSPRpred_logo.jpg' width=10% align=right>\n<p align=left width=70%>\n\nQSPRpred is open-source software libary for building **Quantitative Structure Property\nRelationship (QSPR)** model developed by Gerard van Westen's Computational Drug\nDiscovery group. It provides a unified interface for building QSPR models based on\ndifferent types of descriptors and machine learning algorithms. We developed this\npackage to support our research, recognizing the necessity to reduce repetition in our\nmodel building workflow and improve the reproducibility and reusability of our models.\nIn making this package available here, we hope that it may be of use to other\nresearchers as well. QSPRpred is still in active development, and we welcome\ncontributions and feedback from the community.\n\nQSPRpred is designed to be modular and extensible, so that new functionality can be\neasily added. A command line interface is available for basic use cases to quickly,\nexplore varying scenarios. For more advanced use cases, the Python API offers extra\nflexibility and control, allowing more complex workflows and additional features.\n\nInternally, QSPRpred relies heavily on the <a href=\"https://www.rdkit.org\">RDKit</a>\nand <a href=\"https://scikit-learn.org/stable/\">scikit-learn</a> libraries. Furthermore,\nfor scikit-learn model saving and loading, QSPRpred\nuses <a href=\"https://github.com/OlivierBeq/ml2json\">ml2json</a> for safer and\ninterpretable model serialization. QSPRpred is also interoperable\nwith <a href=\"https://github.com/OlivierBeq/Papyrus-scripts\">Papyrus</a>, a large scale\ncurated dataset aimed at bioactivity predictions, for data collection. Models developed\nwith QSPRpred are compatible with the group's *de novo* drug design\npackage <a href=\"https://github.com/CDDLeiden/DrugEx/\">DrugEx</a>.\n\n\nQuick Start\n===========\n\n## Installation\n\nQSPRpred can be installed with pip like so (with python >= 3.10):\n\n```bash\npip install qsprpred\n```\n\nNote that this will install the basic dependencies, but not the optional dependencies.\nIf you want to use the optional dependencies, you can install the package with an\noption:\n\n```bash\npip install qsprpred[<option>]\n```\n\nThe following options are available:\n\n- extra : include extra dependencies for PCM models and extra descriptor sets from\n packages other than RDKit\n- deep : include deep learning models (torch and chemprop)\n- chemprop: include the ChemProp integration (only ChemProp versions < 2.0.0 supported at the moment)\n- full : include all optional dependecies (requires cupy, `pip install cupy-cudaX`,\n replace X with your [cuda version](https://docs.cupy.dev/en/stable/install.html))\n\n### Note on PCM Modelling\n\nIf you plan to optionally use QSPRpred to calculate protein descriptors for PCM, make\nsure to also install Clustal Omega. You can get it via `conda` (**for Linux and MacOS\nonly**):\n\n```bash\n\nconda install -c bioconda clustalo\n```\n\nor install MAFFT instead:\n\n```bash\nconda install -c biocore mafft\n```\n\nThis is needed to provide multiple sequence alignments for the PCM descriptors. If\nWindows is your platform of choice, these tools will need to be installed manually or a\ncustom implementation of the `MSAProvider` class will have to be made.\n\n## Use\n\nAfter installation, you will have access to various command line features and you can\nuse the Python API directly (\nsee [Documentation](https://cddleiden.github.io/QSPRpred/docs/)). For a quick start, you\ncan also check out the [Jupyter notebook tutorials](./tutorials/README.md), which\ndocument the use of the Python API to build different types of models. The tutorials as\nwell as the documentation are still work in progress, and we will be happy for any\ncontributions where it is still lacking.\n\nContributions\n=============\n\nContributions and issue reports are more than welcome. Pull requests can be made\ndirectly to the `main` branch and we will transfer them\nto [contrib](https://github.com/CDDLeiden/QSPRpred/tree/contrib) when scheduled for the\nnext release.\n\nWorkflow\n========\n\n\nCurrent Development Team\n========================\n\n- [H. van den Maagdenberg](https://github.com/HellevdM)\n- [M. Sicho](https://github.com/martin-sicho)\n- [L. Schoenmaker](https://github.com/LindeSchoenmaker)\n- [O. B\u00e9quignon](https://github.com/OlivierBeq)\n- [S. Luukkonen](https://github.com/sohviluukkonen)\n- [M. Gorosiola Gonz\u00e1lez](https://github.com/gorostiolam)\n- [D. Araripe](https://github.com/David-Araripe)\n- [C. Jespers](https://github.com/ChielJespers)\n- [A. 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