Welcome to moldrug’s documentation!

MolDrug

Documentation
Tutorials
CI/CD
Build
Source Code
Python Versions
Dependencies
License
Downloads

Description

MolDrug (from Molecule to Drug) is a Python package for drug-oriented optimization in the chemical space. It uses a Genetic Algorithm (GA) as a search engine in the chemical space and CReM library as chemical structure generator.

You can try it out before any installation on Binder.

You can use MolDrug-Dashboard to process the results.

Documentation

The installation instructions, documentation and tutorials can be found online on ReadTheDocs.

Issues

If you have found a bug, please open an issue on the GitHub Issues.

Discussion

If you have questions on how to use MolDrug, or if you want to give feedback or share ideas and new features, please head to the GitHub Discussions.

Citing MolDrug

Please refer to the citation page on the documentation.

Funding

This project received funding from Marie Skłodowska-Curie Actions. It was developed in the Computational Biophysics Group of Saarland University in collaboration with the pharmaceutical company Boehringer Ingelheim.

User guide

• Installation
  • Via pip (standard)
  • Via conda
  • AutoDock-Vina dependency and related software
  • Work with a docker container
• Citation
• About Us
  • Project Origins
• Changelog
  • unreleased
  • 3.7.2 - 2024.01.22
  • 3.7.1 - 2024.01.14
  • 3.7.0 - 2024.01.14
  • 3.6.1 - 2023.12.11
  • 3.6.0 - 2023.12.07
  • 3.5.0 - 2023.10.26
  • 3.4.0 - 2023.03.10
  • 3.3.0 - 2022.12.21
  • 3.2.5 - 2022.12.20
  • 3.2.2 - 2022.12.12
  • 3.2.0 - 2022.11.28
  • 3.1.0 - 2022.11.28
  • 3.0.3 - 2022.11.26
  • 3.0.1 - 2022.11.24
  • 3.0.0 - 2022.11.23
  • 2.1.12 - 2022.09.29
  • 2.1.7 - 2022.09.02
  • 2.1.0 - 2022.08.30
  • 2.0.0 - 2022.08.25
  • 1.1.0 - 2022.08.23
  • 1.0.2 - 2022.08.08
  • 1.0.0 - 2022.07.30
  • 0.1.0 - 2022-07-25
  • 0.0.4 - 2022-07-21

Tutorials

• 1. Note on CReM database
• 2. Note on explicit hydrogens and RKit library
• 3. Quickstart
• 4. How-to
  • 4.1. Execute moldrug from the command line
    • 4.1.1. yaml_file anatomy
    • 4.1.2. Run MolDrug
    • 4.1.3. Specify a custom cost function
  • 4.2. Create your own cost function
• 5. MolDrug without receptor
  • 5.1. Imports and creating working directory
  • 5.2. Getting the CReM data base
  • 5.3. Define the function
  • 5.4. Set MolDrug run
• 6. Constraint Docking
  • 6.1. Setting the configuration
  • 6.2. Run MolDrug
  • 6.3. Visualization
  • 6.4. See how it looks with local_only
  • 6.5. Rerun MolDrug
  • 6.6. Change the desirability
• 7. Advance topics
  • 7.1. Optimizing selected positions
  • 7.2. Known issues
• 8. Oncoming
• 9. Derringer-Suich desirability functions
• 10. MolDrug Dashboard
  • 10.1. Inputs
  • 10.2. Home
  • 10.3. Upload pbz2
  • 10.4. Customizing filters and properties
  • 10.5. Interacting with the molecule table
  • 10.6. Ligand-protein network interaction. Upload pdb
  • 10.7. Get 3D view
  • 10.8. Running info

Documentation

• Summary
  • Input data
  • The idea
  • Fitness functions
  • Multi Receptor
• Data
• Fitness
  • Cost()
  • CostMultiReceptors()
  • CostMultiReceptorsOnlyVina()
  • CostOnlyVina()
• Utils
  • Atom
  • CHUNK_VINA_OUT
  • DerringerSuichDesirability()
  • GA
  • Individual
  • LargerTheBest()
  • Local
  • NominalTheBest()
  • SmallerTheBest()
  • VINA_OUT
  • compressed_pickle()
  • confgen()
  • decompress_pickle()
  • deep_update()
  • full_pickle()
  • get_sim()
  • get_similar_mols()
  • import_sascorer()
  • is_iter()
  • lipinski_filter()
  • lipinski_profile()
  • loosen()
  • make_sdf()
  • roulette_wheel_selection()
  • run()
  • tar_errors()
  • to_dataframe()
  • update_reactant_zone()

Indices and tables

• Index

• Module Index

• Search Page