jidushanbojue / Protac-invent

A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

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PROTAC-invent

Source code for our paper "3D Based Generative PROTAC Linker Design with Reinforcement Learning". The code was built based on Reinvent (https://github.com/MolecularAI/Reinvent), DockStream (https://github.com/MolecularAI/DockStream). Thanks a lot for their sharing.

Figure 1. Illustration of PROTAC-invent model

Figure1

Figure 2. The general workflow of PROTAC-invent

Figure1

Figure 3. The workflow of generating and scoring 3D binding conformation of PROTAC

Figure1

Quick start

Installation

  1. Install Conda

  2. Clone this Git repository

  3. Open a shell, and go to the repository and create the Conda environment:

     $ conda env create -f reinvent.yml
    
  4. Activate the environment:

     $ conda activate reinvent.v3.2
    
  5. Install in-house reinvent_scoring

     $ cd reinvent_scoring
    
     $ pip install reinvent_scoring-0.0.73_bq-py3-none-any.whl
    
  6. Open another shell, and clone in-house DockStream repository

    This is the docking component special for Protac-invent.

     $ conda env create -f environment.yml
    

Usage

  1. Edit template Json file (for example in result/LINK_invent/BTK/template.json).

    Templates can be manually edited before using. The only thing that needs modification for a standard run are the file and folder paths. Most running modes produce logs that can be monitored by tensorboard

  2. python input.py template.json

Analyse the results

  1. tensorboard --logdir "progress.log"

    progress.log is the "logging_path" in template.json

We selected top200 solutions for some specific Ternary Complex, such as BTK(PDB code: 6W8I), BAF(PDB code: 6HAX), BRD4(PDB code: 5T35), the result as follow:

  1. For BTK Ternary Complex:

    BTK_poses_top200.sdf: (https://github.com/jidushanbojue/Protac-invent/tree/master/result/LINK_invent/BTK/BTK_poses_top200.sdf)

    BTK_scored_smiles_top200.xlsx (https://github.com/jidushanbojue/Protac-invent/tree/master/result/LINK_invent/BTK/BTK_scored_smiles_top200.xlsx)

  2. For BAF Ternary Complex

    BAF_poses_top200.sdf: (https://github.com/jidushanbojue/Protac-invent/tree/master/result/LINK_invent/BAF/BAF_poses_top200.sdf)

    BAF_scored_smiles_top200.xlsx (https://github.com/jidushanbojue/Protac-invent/tree/master/result/LINK_invent/BAF/BAF_scored_smiles_top200.xlsx)

  3. For BRD4 Ternary Complex

    BRD4_poses_top200.sdf: (https://github.com/jidushanbojue/Protac-invent/tree/master/result/LINK_invent/5T35/BRD4_poses_top200.sdf)

    BTK_scored_smiles_top200.xlsx (https://github.com/jidushanbojue/Protac-invent/tree/master/result/LINK_invent/5T35/BRD4_scored_smiles_top200.xlsx)

We manually selected 25 solutions for BTK, perform MD simulation and MM-GBSA calculation, the result as follow

26_PROTAC_MM-GBSA_value.xlsx (https://github.com/jidushanbojue/Protac-invent/tree/master/result/LINK_invent/BTK/26_PROTAC_MM-GBSA_value.xlsx)

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A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data

License:Apache License 2.0


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