Retrosynthetic Planning with EG-MCTS

Retrosynthetic Planning With Experience-Guided Monte Carlo Tree Search

1. Setup the environment

1) Download the repository

cd EG-MCTS

2) Create a conda environment

conda env create -f environment.yml
conda activate eg_mcts_env

2. Download the data

1) Download the building block set, pretrained one-step retrosynthetic model, and (optional) reactions from USPTO.

Download the files from this link. Put all the files in dataset (origin_dict.csv, uspto.reactions.json, chembl.csv, reaction_graph.gml) under the eg_mcts/dataset directory. Put the folder one_step_model/ under the eg_mcts directory.

3. Install EG-MCTS lib

pip install -e eg_mcts/packages/mlp_retrosyn
pip install -e eg_mcts/packages/rdchiral
pip install -e .

4. Data instruction

1) Trian, vali and test sets

For eMolecules, emol_train_data.txt, emol_vali_data.txt, emol_test_data.txt in eg_mcts/dataset. For chembl, chembl_train_data_T.txt, chembl_train_data_T1.txt, chembl_train_data_T2.txt, chembl_train_data_T^.txt, chembl_train_data_T^^.txt, cheml_vali_data.txt, chembl_test_data.txt in eg_mcts/dataset. For 30 test molecules used in EG-MCTS Versus Literature, we provide their literature routes in SMILES format in eg_mcts/dataset/published_routes_for_30_mol.

2) Pre-trained model

In the folder eg_mcts/saved_EG_fn, we provide the best model best_egn_for_emol.pt for eMolecules. For chembl, , We provide the best models trained in different train data sets.

5. Reproduce experiment results

1) Planning on test set

To plan with EG_MCTS, run the following command,

cd eg_mcts
python EG_MCTS_plan.py

2) Train your BEST EGN

Change the option --test_mols to your training molecule set, create a new random network and change the option --value_model to this new random network.

Then run the command

cd eg_mcts
python EG_MCTS_plan.py  --collect_expe

to collect the synthetic experience of your training molecules, which will be saved in the file experience_dataset/train_experience.pkl. (Or you can change it to your own file.)

And then train this new random network.

Change the option --train_data to your own synthetic experience file.

And then run the command,

python train.py

You will get a new EGN trained for one round.

If you want to train it for multiple time, then repeat the process above.

Don't forget to change the related options.

5. Example usage

See example.py for an example usage.