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

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
https://github.com/binghong-ml/retro_star

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Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search

• Host: GitHub
• URL: https://github.com/binghong-ml/retro_star
• Owner: binghong-ml
• License: mit
• Created: 2020-06-27T03:50:40.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-07-09T01:26:08.000Z (over 2 years ago)
• Last Synced: 2024-08-09T13:20:34.665Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 2.84 MB
• Stars: 128
• Watchers: 5
• Forks: 29
• Open Issues: 14
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Retrosynthetic Planning with Retro*

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search

```bibtex

@inproceedings{chen2020retro,

  title={Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search},

  author={Chen, Binghong and Li, Chengtao and Dai, Hanjun and Song, Le},

  booktitle={The 37th International Conference on Machine Learning (ICML 2020)},

  year={2020}

}

```

#### 1. Setup the environment

##### 1) Download the repository

    

    git clone [email protected]:binghong-ml/retro_star.git

    cd retro_star

    

##### 2) Create a conda environment

    

    conda env create -f environment.yml

    conda activate retro_star_env

#### 2. Download the data

##### 1) Download the building block molecules, pretrained models, and (optional) test data 

Download and unzip the files from this [link](https://www.dropbox.com/s/ar9cupb18hv96gj/retro_data.zip?dl=0), 

and put all the folders (```dataset/```, ```one_step_model/``` and ```saved_models/```) under the ```retro_star``` directory.

#### 3. Install Retro* lib

Install the retrosynthetic planning library with the following commands.

    pip install -e retro_star/packages/mlp_retrosyn

    pip install -e retro_star/packages/rdchiral

    pip install -e .

#### 4. Reproduce experiment results

To plan with Retro*, run the following command,

    cd retro_star

    python retro_plan.py --use_value_fn

    

Ignore the ```--use_value_fn``` option to plan without the learned value function.

You can also train your own value function via,

    python train.py

    

#### 5. Example usage

See ```example.py``` for an example usage.

```python

from retro_star.api import RSPlanner

planner = RSPlanner(

    gpu=-1,

    use_value_fn=True,

    iterations=100,

    expansion_topk=50

)

result = planner.plan('CCCC[C@@H](C(=O)N1CCC[C@H]1C(=O)O)[C@@H](F)C(=O)OC')

```