General-purpose reactivity detection using proton NMR spectra. See also Rx1st, "Reactivity first exploration of chemical space" making use of Reactify's reactivity assignments.
For development, we recommend cloning the repository and installing it in development mode.
git clone https://github.com/croningp/ReactifyYou can use conda to install dependencies first
conda env create --file Reactify/environment.yml
conda activate ReactifyTo install the Reactify package in development mode:
pip install -e ReactifyThe enclosed Jupyter notebook Reactify.ipynb is recommended as an example of how to train the Reactify model and evaluate its performance. We also provide a command line interface to achieve the same, see below.
You can train the Reactify model using the dataset from the original publication. To do
so, download the manuscript dataset and extract reactify_data.tar.xz to the Reactify installation
folder:
cd <installation directory or directory where you cloned Reactify>
wget https://zenodo.org/record/4670997/files/reactify_data.tar.xz?download=1
tar xvf reactify_data.tar.xzAlternatively, manually download the dataset and place it in the installation folder.
You can now launch the training script from anywhere:
# To see the script's full list of command line parameters
python -m Reactify.training -h
# e.g.
python -m Reactify.training --leave_out 1018 --no-plot models/reactify_modelIn addition to the trained model, this script also plots the confusion matrix as well as (optionally) a plot showing the reactivity assignment for each example in the test dataset.
For quick assigment of reactivity, the script in inference.py gives a command-line interface for running inference:
python -m Reactify.inference <path to trained model> <path to reaction spectrum> <path to reactant 1 spectrum> <path to reactant 2 spectrum> ...The manuscript dataset inlcludes a pre-trained model (under data/FinderX-pretrained) that you can use as reference. This model has been trained on the simple chemical (non-photochemical) space only.