Team TaoFuFa

[Skyquek]

In this project, we use genetic algorithm and deep learning to discover existing or new drugs that could bind to COVID-19 main protease (6LU7) and has a logP value that is lower than 5.

[Skyquek]

Conversion

• Microsoft PowerShell 5.0 or above
• NodeJS

How to use

* You may want to finish reading the repo before starting.

Initial generation

1. Run 'Initial Network.ipynb' to get generation 0 SMILES.

For every generation (including initial)

2. Run 'Evaluation and Refinement-localGA.ipynb'

Sharding the sdf files

3. In PyRx, load the sdf files and minimize the molecules, then export it to pdbqt file format.
4. Copy out the .pdbqt files to a seperate folder and run the sharding script (/scripts/folderSplitter/shard.ps1).
5. Copy everything from the /scripts/binding folder to each generated folder.
6. The folder is ready for distribution.

Computing the binding affinity

7. For each folder, run PowerShell in it and run the /scripts/binding/binding.ps1 file.
8. The validation results should be in the output folder.

Consolidation

9. When processing is done, consolidate all the files in each output folder, then copy files in/scripts/conversion to it.
10. Run PowerShell in it and run the /scripts/conversion/conversion.ps1 file.
11. The compute results would be consolidated in the results.csv file of the same folder.

A generation is complete, you may go to 2 to obtain the next gen.

Post processing (optional, but recommended to do for last gen)

12. Run Final Results.ipynb to visualize the data and filter up the best molecule.
13. A file will be created in generations/master_results_table_final.csv. This file can be validated by bioinformatics.