m-mokaya / riop Goto Github PK

This repository contains training data, examples and results reported in https://www.biorxiv.org/content/10.1101/2022.07.15.500218v1.

Our work is built on previously publised work (REINVENT 3.0 - https://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0235-x).

For simplicity, we use a very similar pipeline, therefore you may find it helpful to consult their repository (https://github.com/MolecularAI/Reinvent).

We have provided examples notebooks for creating the input files neccessary to reproduce our results in

./notebooks

We also provide our datasets for specific experiments where the full Chembl dataset is not used, these are available in

./data

Finally, we provide example generated libraries of molecules for each of our experiments in

./results

Below is a breakdown of main experiment reported in our work with their experiment index to reference datasets and results.

*Instructions, examples and tutorials for DrIOP are available at https://github.com/m-mokaya/RIOP_DrIOP

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.v3.0
   

5. Use the tool. Installation is expected to take a few minutes.

• Python 3.7
• Cuda-enabled GPU
• REINVENT and RIOP have been tested on Linux

We have included a series of notebooks that allow show how we did each of our experiments.

There is another repository containing useful jupyter notebooks related to REINVENT called ReinventCommunity. Note, that it uses a different conda environment to execute, so you have to set up a separate environment.

For concrete examples, you can check out the Jupyter notebook examples in the ReinventCommunity repo. Running each example will result in a template file.There are templates for many running modes. Each running mode can be executed by python input.py some_running_mode.json after activating the environment.

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.