rexgen_direct

Template-free prediction of organic reaction outcomes using graph convolutional neural networks

Described in A graph-convolutional neural network model for the prediction of chemical reactivity

Dependencies

Python (trained/tested using 2.7.6, visualization/deployment compatible with 3.6.1)
Numpy (trained/tested using 1.12.0, visualization/deployment compatible with 1.14.0)
Tensorflow (trained/tested using 1.3.0, visualization/deployment compatible with 1.6.0)
RDKit (trained/tested using 2017.09.1, visualization/deployment compatible with 2017.09.3)
Django (visualization compatible with 2.0.6)

note: there may be some issues with relative imports when using Python 2 now; this should be easy to resolve by removing the periods preceding package names

Instructions

Looking at predictions from the test set

cd into the website folder and start the Django app using python manage.py runserver. Go to http://localhost:8000/visualize in a browser to use the interactive visualization tool

Using the trained models

You can use the fully trained model to predict outcomes by following the example at the end of rexgen_direct/rank_diff_wln/directcandranker.py

Retraining the models

Look at the two text files in rexgen_direct/core_wln_global/notes.txt and rexgen_direct/rank_diff_wln/notes.txt for the exact commands used for training, validation, and testing. You will have to unarchive the data files after cloning this repo.