This is open-source codebase for MEEA*-PC (https://www.nature.com/articles/s42004-024-01133-2). MEEA* is proposed to incorporate the exploratory behavior of MCTS into A* by providing a look-ahead search. Path consistency is adopted as a regularization to improve the generalization performance of heuristics. Details about the search algorithm is summarized in the following figure.
Extensive experimental results on
Dataset | Success Rate | Dataset | Success Rate |
---|---|---|---|
USPTO | logS | ||
BBBP | ClinTox | ||
logP | DPP4 | ||
BACE | Ames | ||
Toxicity LD50 | SVS |
rdkit==2022.9.3
torch==1.13.1
pandas==1.3.5
numpy==1.21.5
tqdm==4.64.1
rdchiral needs to be installed by
pip install -e rdchiral
Download the building block molecules, and pretrained models from https://drive.google.com/file/d/1lXtRKRGETEYz0bTRAsl1LsBYGD20MM9O/view?usp=drive_link
To test on regular organic molecule datasets
python MEEA_PC_parallel.py
To test on natural products
python MEEA_PC_NPs_parallel.py
If you find this repo useful, please cite our paper:
@article{zhao2024efficient,
title={Efficient retrosynthetic planning with MCTS exploration enhanced A* search},
author={Zhao, Dengwei and Tu, Shikui and Xu, Lei},
journal={Communications Chemistry},
volume={7},
number={1},
pages={52},
year={2024},
publisher={Nature Publishing Group UK London}
}
We appreciate the following github repos greatly for their valuable code base implementations:
https://github.com/binghong-ml/retro_star