A package for predict scaffold of NR inhibitors
pisnrs is a Python module for NR inhibitors prediction built on top of RDKit, scikit-learn and distributed under the MIT license.
pisnrs requires: - Python (>= 2.6) - rdkit (>= 2018.03.2.0) - scikit-learn (>= 0.19.1) - pandas (>= 0.23.1) User installation
You can install pisnrs using anaconda.
Introduction to anaconda
Conda is an open-source, cross-platform, software package manager. It supports the packaging and distribution of software components, and manages their installation inside isolated execution environments. It has several analogies with pip and virtualenv, but it is designed to be more "python-agnostic" and more suitable for the distribution of binary packages and their dependencies.
How to get conda
The easiest way to get Conda is having it installed as part of the Anaconda Python distribution. A possible (but a bit more complex to use) alternative is provided with the smaller and more self-contained Miniconda. The conda source code repository is available on github and additional documentation is provided by the project website.
How to install RDKit with Conda
Creating a new conda environment with the RDKit installed requires one single command similar to the following::
$ conda create -c rdkit rdkit
Install scikit-learn using pip:: pip install -U scikit-learn
conda install scikit-learn
If you already have a working installation of rdkit and scikit-learn, the easiest way to install pisnrs is using
pip install pisnrs
- Official source code repo: https://github.com/pisnrs/pisnrs - Download releases: https://pypi.python.org/pypi/pisnrs Source code ~~~~~~~~~~~ You can check the latest sources with the command:: git clone https://github.com/pisnrs/pisnrs.git
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