Implementation of Retrosynthesis Prediction with LocalRetro developed by prof. Yousung Jung group at KAIST (contact: [email protected]).

Shuan Chen (contact: [email protected])

• Python (version >= 3.6)
• Numpy (version >= 1.16.4)
• PyTorch (version >= 1.0.0)
• RDKit (version >= 2019)
• DGL (version >= 0.5.2)
• DGLLife (version >= 0.2.6)

Create a virtual environment to run the code of LocalRetro.
Install pytorch with the cuda version that fits your device.

cd LocalRetro
conda create -c conda-forge -n rdenv python=3.7 -y
conda activate rdenv
conda install pytorch cudatoolkit=11.3 -c pytorch -y
conda install -c conda-forge rdkit -y
conda install -c dglteam dgl-cuda11.3 -y
pip install dgllife

Shuan Chen and Yousung Jung. Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention, JACS Au 2021.

We cleaned the code and made the template more simplied, which yields 658 local reaction templates for USPTO_50K dataset and 20,221 local reaction templates for USPTO_MIT dataset. Therefore we tested the top-k accuracy again and the results are updated at the bottom of README.md. The training takes around 100 minutes on NVIDIA GeForce RTX 3090

Currently, we are cleaning up the codes, and the codes will be uploaded back afterwards.

See the README in ./data to download the raw data files for training and testing the model.

A two-step data preprocessing is needed to train the LocalRetro model.

First go to the data processing folder

cd preprocessing

and extract the reaction template with specified dataset name (default: USPTO_50K).

python Extract_from_train_data.py -d USPTO_50K

This will give you four files, including (1) atom_templates.csv (2) bond_templates.csv (3) template_infos.csv (4) template_rxnclass.csv (if train_class.csv exists in data folder)

By running

python Run_preprocessing.py -d USPTO_50K

You can get four preprocessed files, including (1) preprocessed_train.csv (2) preprocessed_val.csv (3) preprocessed_test.csv (4) labeled_data.csv

Go to the localretro folder

cd ../scripts

and run the following to train the model with specified dataset (default: USPTO_50K)

python Train.py -d USPTO_50K

The trained model will be saved at LocalRetro/models/LocalRetro_USPTO_50K.pth

To use the model to test on test set, simply run

python Test.py -d USPTO_50K

to get the raw prediction file saved at LocalRetro/outputs/raw_prediction/LocalRetro_USPTO_50K.txt
Finally you can get the reactants of each prediciton by decoding the raw prediction file

python Decode_predictions.py -d USPTO_50K

The decoded reactants will be saved at LocalRetro/outputs/decoded_prediction/LocalRetro_USPTO_50K.txt
and LocalRetro/outputs/decoded_prediction_class/LocalRetro_USPTO_50K.txt

*AT = Augmented Transformer