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REINVENT 4

Description

REINVENT is a molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. REINVENT uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.

A paper describing the software has been published as Open Access in the Journal of Cheminformatics: Reinvent 4: Modern AI–driven generative molecule design. See AUTHORS.md for references to previous papers.

Requirements

REINVENT is being developed on Linux and supports both GPU and CPU. The Linux version is fully validated. REINVENT on Windows and MacOSX supports both GPU and CPU but is only partially tested on these platforms and therefore support is limited.

The code is written in Python 3 (>= 3.10). The list of dependencies can be found in the repository (see also Installation below).

A GPU is not strictly necessary but strongly recommended for performance reasons especially for transfer learning and model training. Reinforcement learning (RL) requires the computation of scores where most scoring components run on the CPU. Thus, a GPU is less important for RL (depending on how much time is spent on the CPU).

Note that if no GPU is installed in your computer the code will run on the CPU automatically. REINVENT supports NVIDIA GPUs and also some AMD GPUs. For most design tasks a memory of about 8 GiB for both CPU main memory and GPU memory is sufficient.

Installation

  1. Clone this Git repository.
  2. Install a compatible version of Python, for example with Conda (other virtual environments like Docker, pyenv, or the system package manager work too). 
    conda create --name reinvent4 python=3.10
conda activate reinvent4
  3. Change directory to the repository and install the dependencies from the lockfile: 
    pip install -r requirements-linux-64.lock
    1. Optional: if you want to use AMD GPUs on Linux you would need to install the ROCm PyTorch version manually after installation of the dependencies in point 3, e.g. 
      pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/rocm5.7
    2. Optional: use requirements file requirements-macOS.lock for MacOSX.
  4. Install the tool. The dependencies were already installed in the previous step, so there is no need to install them again (flag `--no-deps). If you want to install in editable mode (changes to the code are automatically available), add -e before the dot. 
    pip install --no-deps .
  5. Test the tool. The installer has added a script reinvent to your PATH. 
    reinvent --help

Basic Usage

REINVENT is a command line tool and works principally as follows

reinvent -l sampling.log sampling.toml

This writes logging information to the file sampling.log. If you wish to write this to the screen, leave out the -l sampling.log part. sampling.toml is the configuration file. The main user format is TOML as it tends to be more use friendly. JSON can be used too, add -f json, but a specialised editor is recommended as the format is very sensitive to minor changes.

Sample configuration files for all run modes are located in config/toml in the repository and file paths in these files would need to be adjusted to your local installation. In particular, ready made prior models are located in priors and you would choose a model and the appropriate run mode depending on the research problem you are trying to address. There is additional information in config/toml in several *.md files with instructions on how to configure the TOML file. Internal priors can be referenced with a dot notation (see reinvent/prior_registry.py).

Tutorials / Jupyter notebooks

Basic instructions can be found in the comments in the config examples in config/toml.

Notebooks are provided in the notebooks/ directory. Please note that we provide the notebooks in jupytext "light script" format. To work with the light scripts you will need to install jupytext. A few other packages will come in handy too.

pip install jupytext mols2grid seaborn

The Python files in notebooks/ can then be converted to a notebook e.g.

jupytext -o Reinvent_demo.ipynb Reinvent_demo.py

Updating dependencies

Update the lock files with pip-tools (please, do not edit the files manually):

pip-compile --extra-index-url=https://download.pytorch.org/whl/cu121 --extra-index-url=https://pypi.anaconda.org/OpenEye/simple --resolver=backtracking pyproject.toml

To update a single package, use pip-compile --upgrade-package somepackage (see the documentation for pip-tools).

Scoring Plugins

The scoring subsystem uses a simple plugin mechanism (Python native namespace packages). If you wish to write your own plugin, follow the instructions below. There is no need to touch any of the REINVENT code. The public repository contains a contrib directory with some useful examples.

  1. Create /top/dir/somewhere/reinvent\_plugins/components where /top/dir/somewhere is a convenient location for you.
  2. Do not place a __init__.py in either reinvent_plugins or components as this would break the mechanism. It is fine to create normal packages within components as long as you import those correctly.
  3. Place a file whose name starts with comp_* into reinvent_plugins/components. Files with different names will be ignored i.e. not imported. The directory will be searched recursively so structure your code as needed but directory/package names must be unique.
  4. Tag the scoring component class(es) in that file with the @add_tag decorator. More than one component class can be added to the same comp_ file. See existing code.
  5. Tag at most one dataclass for parameters in the same file, see existing code. This is optional.
  6. Set or add /top/dir/somewhere to the PYTHONPATH environment variable or use any other mechanism to extend sys.path.
  7. The scoring component should now automatically be picked up by REINVENT.

Unit and Integration Tests

This is primarily for developers and admins/users who wish to ensure that the installation works. The information here is not relevant to the practical use of REINVENT. Please refer to Basic Usage for instructions on how to use the reinvent command.

The REINVENT project uses the pytest framework for its tests. Before you run them you first have to create a configuration file for the tests.

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. Make sure that you set MAIN_TEST_PATH to a non-existent directory. That 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.

Once you have a configuration and your license can be read, you can run the tests.

$ pytest tests --json /path/to/config.json --device cuda