semvec/pypi-packages huggingface dataset:
CancerDockPredict 0.0.1
Python package that calculates Lipinsky descriptors, predicts pIC50 and performs docking
License: MIT

# CancerDockPredict
```markdown
This is a simple platform for computing Lipinsky's Rule of five using the rdkit package, predicting pIC50 of canonical SMILES that are potential targets against Oestrogen receptor alpha protein as ant-prostate cancer agaents using apreformatted RandomForest model, and docking of the canonical SMILE with the Oestrogen receptor alpha protein using Audodock Vina package. 
```
### Purpose of the Package
```markdown
The purpose of the package is to provide a unified platform for computing prostate cancer drug likeness indicess and performing docking on the same compounds. 
```
### Features
```markdown
Important chemoinformatics features of Oestrogen receptor alpha antagonists such as:
    - Lipinsky descriptors
    - Prediction of pIC50
    - Docking and visiualization 
```
### Getting Started
The package is found on pypi hence can be installed with pip

#### Installation
```markdown
It is important to ensure that all the required dependencies are installed in your working environment. It would be much easier if you create a conda environment before installation of packages. The following packages are required, **pymol**, **rdkit**, **pandas**, **padelpy**, **joblib**, **csv**, **meeko**, **Autodock Vina**, **java**, **scipy**, and **scikit-learn**.
```
```bash
conda create -n CancerDockPredict
conda activate CancerDockPredict
```
```markdown
Then, install pymol before installing other packages:
```
```bash
conda install -c conda-forge pymol-open-source

conda install -c conda-forge openbabel
conda install -c cyclus java-jre
conda install -c bioconda autodock-vina

pip install rdkit pandas padelpy joblib python-csv meeko scikit-learn scipy

pip install CancerDockPredict
```


### Usage
#### Computating Lipinsky descriptors
```python
>>>from CancerDockPredict import calculate_lipinski_descriptors
>>>calculate_lipinski_descriptors("Oc1ccc2c(c1)S[C@H](c1ccco1)[C@H](c1ccc(OCCN3CCCCC3)cc1)O2")
```
#### Predictioning pIC50
```python
>>>from CancerDockPredict import predict_pIC50
>>>predict_pIC50("Oc1ccc2c(c1)S[C@H](c1ccco1)[C@H](c1ccc(OCCN3CCCCC3)cc1)O2")
```
#### Docking with protein, pdb_id 5gs4
```python
>>>from CancerDockPredict import prot_lig_docking
>>>prot_lig_docking("Oc1ccc2c(c1)S[C@H](c1ccco1)[C@H](c1ccc(OCCN3CCCCC3)cc1)O2")
```
#### Visualization of docking results
```python
>>>from CancerDockPredict import vizualize_dock_results
>>>vizualize_dock_results()
```


```markdown
### Contribution
We welcome any contributions. Should you notice a bug, please let us know through issues in the github repository, [https://github.com/clabe-wekesa/CancerDockPredict/issues](issues)


### Authors
Edwin mwakio, Dr. Clabe Wekesa and Dr. Patrick Okoth
Department of Biological Sciences, Masinde Muliro University of Science and Technology, [https://www.mmust.ac.ke/](MMUST) 
```
astoria 0.11.3
Robot Management System for Student Robotics Kit
License: MIT
Requires Python: >=3.8,<4.0

# Astoria

![Tests](https://github.com/srobo/astoria/workflows/Tests/badge.svg)
![Build and Publish docs](https://github.com/srobo/astoria/workflows/Build%20and%20Publish%20docs/badge.svg)
[![PyPI version](https://badge.fury.io/py/astoria.svg)](https://badge.fury.io/py/astoria)
[![MIT license](https://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](https://opensource.org/licenses/MIT)
![Bees](https://img.shields.io/badge/bees-110%25-yellow.svg)

Robot Management System for Student Robotics 2022 kit.

For more information, please see the [documentation](https://srobo.github.io/astoria).

## Contributions

This project is released under the MIT Licence. For more information, please see LICENSE.

The CONTRIBUTORS file can be generated by executing CONTRIBUTORS.gen. This generated file contains a list of people who have contributed to Astoria.