Python package that calculates Lipinsky descriptors, predicts pIC50 and performs docking
Project description
MLDockKit
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 a preformatted RandomForest model, and docking of the canonical SMILE with the Oestrogen receptor alpha protein using Audodock Vina package.
Purpose of the Package
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
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
Pre-requisites
Installation of Vina requires boost and swig
Importantly: Install pymol as the first package in the new environment, this is due to environmental package conflict.
Installation
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, meeko, Autodock Vina, java, scipy, and scikit-learn.
conda create -n MLDockKit
conda activate MLDockKit
Then, install pymol before installing other packages:
conda install -c conda-forge pymol-open-source
conda install -c conda-forge openbabel
conda install -c cyclus java-jre
pip install -U numpy vina
pip install rdkit pandas padelpy joblib meeko scikit-learn scipy
pip install MLDockKit
Usage
Computating Lipinsky descriptors
>>>from MLDockKit import calculate_lipinski_descriptors
>>>calculate_lipinski_descriptors("Oc1ccc2c(c1)S[C@H](c1ccco1)[C@H](c1ccc(OCCN3CCCCC3)cc1)O2")
Predictioning pIC50
>>>from MLDockKit import predict_pIC50
>>>predict_pIC50("Oc1ccc2c(c1)S[C@H](c1ccco1)[C@H](c1ccc(OCCN3CCCCC3)cc1)O2")
Docking with protein, pdb_id 5gs4
>>>from MLDockKit import prot_lig_docking
>>>prot_lig_docking("Oc1ccc2c(c1)S[C@H](c1ccco1)[C@H](c1ccc(OCCN3CCCCC3)cc1)O2")
Visualization of docking results
This opens pymol for visulization and analysis. If you need help on pymol analysis please have a look on pymol documentation
>>>from MLDockKit import vizualize_dock_results
>>>vizualize_dock_results()
Acknowledgment
Autodock Vina and pymol were greatily used in writing the codes for molecular docking and visualization. If you use these functions in your work, please cite the original publications for vina and pymol
We extracted part of Angel Ruiz Moreno's Jupyter_Dock Jupyter Dock to include it in our visualization function.
Contribution
We welcome any contributions. Should you notice a bug, please let us know through issues in the, GitHub Issue Tracker
Authors
Edwin mwakio, Dr. Clabe Wekesa and Dr. Patrick Okoth,
Department of Biological Sciences, Masinde Muliro University of Science and Technology
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