LocalRetro

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

Announcements

2024.05.30 update

The open-source license and part of the codes are removed from our project on 2024.05.30.

Developer

Shuan Chen (contact: [email protected])

Requirements

• 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)

Requirements

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=10.2 -c pytorch -y
conda install -c conda-forge rdkit -y
pip install dgl
pip install dgllife

Publication

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

Usage

[1] Download the raw data of USPTO-50K or USPTO-MIT dataset

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

[2] Data preprocessing

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

1) Local reaction template derivation

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)

2) Assign the derived templates to raw data

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

[3] Train LocalRetro model

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

[4] Test LocalRetro model

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