rnaimehaom / riop

Introduction

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.

Experiment	Description
1	Single property shift
2	%-representation
3	TPSA shift with rIOP
4	QED optimisation with rIOP *
5	Generating simple and complex substructures
6	Effects of SMILES on model performance

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

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.v3.0
   

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

System Requirements

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

Tutorials / jupyter notebooks

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.

Usage

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.

Tests

The REINVENT project uses the unittest framework for its tests; before you run them you first have to create a configuration, which the tests will use. In the project directory, create a config.json file in the configs/ directory; you can use the example config (example.config.json) as a base. The simplest way is to make a copy of the example.config.json and name it config.json. At this point, REINVENT can be executed. If you want to further run the unit tests, relevant paths will need to be specified in your 'config.json' file, e.g, if testing reinforcement learning, the corresponding unit tests will require a prior: specify the path to the prior in the "PRIOR_PATH" field.

Important: Make sure that you set MAIN_TEST_PATH to a non-existent directory; it is where temporary files will be written during the tests; if it is set to an existing directory, that directory will be removed once the tests have finished.

Some tests require a proprietary OpenEye license; you have to set up a few things to make the tests read your license. The simple way is to just set the OE_LICENSE environment variable to the path of the file containing the license. If you just want to set the license in the reinvent_scoring Conda environment, it is a bit more complicated, but you only have to do it once.

(reinvent-scoring) $ cd $CONDA_PREFIX
$ mkdir -p etc/conda/activate.d
$ mkdir -p etc/conda/deactivate.d

Put the following in etc/conda/activate.d/env_vars.sh.

#!/bin/sh
export OE_LICENSE='</path/to/your/oe_license/file>'

And put the following in etc/conda/deactivate.d/env_vars.sh.

#!/bin/sh
unset OE_LICENSE

Once you have created the files, deactivate and re-activate the environment, and echo $OE_LICENSE should output the path to the license file. Once you have a configuration and your license can be read, you can run the tests.

$ python main_test.py