AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
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. At its heart, 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.
REINVENT is being developed on and primarily for Linux and supports both GPU and CPU. The Linux version is fully validated. REINVENT runs on Windows with both GPU and CPU but this platform is mostly untested. MacOSX is only supported on the CPU.
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/model training. It should be noted that reinforcement learning (RL) requires the computation of scores. Most scoring components run on the CPU thus a GPU is of less importance 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 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.
conda create --name reinvent4 python=3.10
conda activate reinvent4
pip install -r requirements-linux-64.lock
pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/rocm5.7
pip install --no-deps .
reinvent
to your PATH.
reinvent --help
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
of the repository and file paths therein 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.
Jupyter
notebooksBasic instructions can be found in the comments in the config examples in config/toml
.
Notebooks will be provided in the notebook/
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 notebook/
can then be converted to a notebook e.g.
jupytext --to ipynb -o Reinvent_demo.ipynb Reinvent_demo.py
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).
The scoring subsystem uses a simple plugin mechanism (Python native namespace packages). If you wish to write your own plugin, follow the instructions below. The public repository contains a contrib directory with some useful examples.
/top/dir/somewhere/reinvent\_plugins/components
where /top/dir/somewhere
is a convenient location for you.__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.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./top/dir/somewhere
to the PYTHONPATH
environment variable or use any other mechanism to extend sys.path
.This is primarily for developers and admins/users who wish to ensure that the
installation principally 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 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. 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