Efficient retrosynthetic planning with MCTS exploration enhanced A* search

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 $10$ molecule datasets demonstrate the effectiveness of MEEA*. Moreover, for natural products, MEEA*-PC successfully identifies bio-retrosynthetic pathways for $97.68%$ test compounds. Experiment results on each dataset is summarized in the following table:

Dataset	Success Rate	Dataset	Success Rate
USPTO	$94.74%$	logS	$80.34%$
BBBP	$66.88%$	ClinTox	$60.51%$
logP	$73.72%$	DPP4	$96.04%$
BACE	$56.32%$	Ames	$78.74%$
Toxicity LD50	$72.94%$	SVS	$73.01%$

Requirements

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

Testing

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

Citation

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}
}

Acknowledgement

We appreciate the following github repos greatly for their valuable code base implementations:

https://github.com/binghong-ml/retro_star

https://github.com/junsu-kim97/self_improved_retro

https://github.com/prokia/BioNavi-NP