| Name | cando-py JSON |
| Version |
2.3.4
JSON |
| download |
| home_page | None |
| Summary | CANDO is a unique computational drug discovery, design, and repurposing platform. |
| upload_time | 2025-07-25 20:42:01 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.12,>=3.7 |
| license | Copyright 2019 William Mangione
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
| keywords |
drug discovery
drug design
translational bioinformatics
drug repurposing
computational biology
biomedical informatics
modeling and simulation
machine learning
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://travis-ci.com/ram-compbio/CANDO)
[](https://codecov.io/gh/ram-compbio/CANDO)
[](https://anaconda.org/ram-compbio/cando)
[](https://anaconda.org/ram-compbio/cando)
[](https://anaconda.org/ram-compbio/cando)
# CANDO
Computational Analysis of Novel Drug Opportunities
---
## Background
CANDO is an unique and innovative multiscale therapeutic discovery, repurposing, and design platform.
For additional information, publications, and funding sources, please check out our [website](http://protinfo.compbio.buffalo.edu/cando/) or our [ACKNOWLEDGEMENTS](https://github.com/ram-compbio/CANDO/blob/master/ACKNOWLEDGEMENTS.md).
## Install
You may download the source code via the releases or cloning the git repository. However, we suggest using anaconda to install the CANDO package, as this is the easiest and quickest way to start using our platform!
The CANDO package relies on multiple "conda-forge" dependencies. Therefore, we require that you add "conda-forge" to your anaconda channels:
`conda config --add channels conda-forge`
Then you can install CANDO using the following command:
`conda install -c ram-compbio cando`
## Tutorial
There is a CANDO tutorial available as a Jupyter notebook.
This notebook can be found [here](https://github.com/ram-compbio/CANDO/blob/master/CANDO_tutorial.ipynb) in this repo.
It can also be downloaded from anaconda:
`anaconda download ram-compbio/CANDO_tutorial`
## Documentation
CANDO API can be found [here](https://github.com/ram-compbio/CANDO/blob/master/docs)
## Test
You can test your install by running our script:
[run_test.py](https://github.com/ram-compbio/CANDO/blob/master/run_test.py)
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