karanicolaslab/vscreenml

This repository contains the implementation of a novel machine learning classifier trained on the Dataset of Congruent Inhibitors and Decoys (D-COID))

vScreenML is a tool`that allows rescoring of virtual screening hits to prune out false positives.

For more deatils, check out PNAS paper at PNAS paper

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed the following packages OpenEye, ChemAxon calcx, ChemAxon structurecheck, MGLTOOLS, BINANA
• Set up Rosetta to work with vscreenml
  • Request license here https://els.comotion.uw.edu/express_license_technologies/rosetta (academic license is free). Once you obtain the license, you can download Rosetta here https://www.rosettacommons.org/software/license-and-download and do the following
  • In order to use this tool, it's better to build Rosetta from scratch. Once Rosetta repositiory is downloaded, before compiling do the following:
    1. from your terminal change into path/to/Rosetta/main/source/src/apps/pilot/

       cd path/to/Rosetta/main/source/src/apps/pilot/
       

    2. Inside path/to/Rosetta/main/source/src/apps/pilot/ create a new directory "vscreenml_rosetta_extension"

       mkdir vscreenml_rosetta_extension
       

    3. copy vscreenml_rosetta_extension/vscrenml_interface_ddg.cc into path/to/Rosetta/main/source/src/apps/pilot/vscreenml_rosetta_extension

       cp /path/to/vscreenml/vscreenml_rosetta_extension/vscrenml_interface_ddg.cc vscreenml_rosetta_extension
       

    4. add the json formated text below to path/to/Rosetta/main/source/src/pilot_apps.src.settings.all

       "pilot/vscreenml_rosetta_extension" : [
           "vscrenml_interface_ddg",
        ],
       

    ```
    

    5. Compile Rosetta by changing to the source directory and running: python scons bin mode=release

Installing vScreenML

• Clone this repository

• Install all the required python packages

  conda create --name myenv --file requirements.txt
  

• Install all the above dependencies and update configuration/congif.py file paths pointing to this dependencies.

Once all the dependencies have been installed, vscreenml is ready to use.

Using vScreenML

To use vScreenML, follow these steps: For example, in order go get the vscreenml score of "mini_complex_sample.pdb" present in "test" directory python vscreenmlscore.py test sample Add run commands and examples you think users will find useful. Provide an options reference for bonus points!

Contributing to vScreenML

To contribute to vScreenML, follow these steps:

1. Fork this repository.
2. Create a branch: git checkout -b <branch_name>.
3. Make your changes and commit them: git commit -m '<commit_message>'
4. Push to the original branch: git push origin vscreenml/<location>
5. Create the pull request.

Alternatively see the GitHub documentation on creating a pull request.

Contact

If you want to contact me you can reach me at dryusufadeshina@gmail.com.

License

This project uses the following license: MIT License.