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This package facilitates molecular docking simulations aimed at analyzing interactions between a target biological system and a collection of potential drug molecules. By leveraging computational algorithms, it ranks these molecules based on docking scores and interaction energies, providing insights into their suitability as drug candidates.

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kumaranu/MolecularDockingKit

MolecularDockingKit

MolecularDockingKit is a Python tool designed for molecular docking computations. It provides a simple and efficient way to perform docking simulations and analyze the results.

Overview

The repository contains scripts and supplementary files necessary for running molecular docking simulations and analyzing the output data. Below is a brief overview of the main components:

  • Main Script: make.bash
  • Supplementary Scripts:
    • main.py
    • getSmilesFromFile.py
    • xyzFromSmiles.py
    • getScores.tcsh
    • call_combine.tcsh
    • combine.py
    • run.slurm (inside the energy_protein directory)
    • getEnergy.py
    • run_template.slurm

Usage

To use MolecularDockingKit, follow these steps:

  1. Prepare input files:

    • Create a drugs.txt file containing initial data.
    • Obtain a 3sxr_dasatinib_removed.pdb file representing the protein without dasatinib.
    • Set up a directory named energy_protein with a run.py script for submitting protein jobs.
    • Provide a template file named prm-template.prm for the prm input file used in docking calculations.
  2. Execute the main script make.bash to initiate the docking computations.

  3. Follow the instructions provided in the comments of each script to understand their specific functions and requirements.

Contributing

Contributions to MolecularDockingKit are welcome! If you encounter any issues or have suggestions for improvement, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.

Author

Anup Kumar

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This package facilitates molecular docking simulations aimed at analyzing interactions between a target biological system and a collection of potential drug molecules. By leveraging computational algorithms, it ranks these molecules based on docking scores and interaction energies, providing insights into their suitability as drug candidates.

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