jjljkjljk / baseline

Baselines for Retrosynthetic Planning With Experience-Guided Monte Carlo Tree Search

1. Setup the environment

1) Download the repository

cd baseline

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 baseline/dataset directory. Put the folder one_step_model/ under the baseline directory.

3. Install lib

pip install -e baseline/packages/mlp_retrosyn
pip install -e baseline/packages/rdchiral

4. Data instruction

Three test sets in dataset.

5. Reproduce experiment results

cd baseline

DFS_plan.py for greedy DFS. DFPN_E_plan.py for dfpn-e. MCTS_rollout_plan.py for MCTS-rollout.

You can change the plan class's test_file_name to change the test file. The hyperparameter settings are detailed in the properties of the classes.