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https://github.com/brsynth/rp3_dcache


https://github.com/brsynth/rp3_dcache

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# Database based cache to dope data retrieval for RetroPath RL

Here it is a two components cache system to dope the result retrieval from reaction rules
results, to be used with RetroPath RL. The 2 components are:

- A python module to be imported in RetroPath RL
- A Mongo database, which should be started independently (see "How to run the database in
the background")

Notice : at reading time, some of following information might be deprecated!

## Needed components

- docker : https://docs.docker.com/install/
- docker-compose : https://docs.docker.com/compose/install/
- conda : https://docs.conda.io/projects/conda/en/latest/user-guide/install/

## Setting up python environment

Basically: one needs a conda environment with following packages:
- rdkit
- pymongo
- pytest

```bash
conda create --name myenv python=3.6
source activate myenv
conda install --channel rdkit rdkit=2018.09.1.0
conda install --channel conda-forge pytest
conda install pytest
conda install pymongo
conda install pyyaml
```

## Install as a packages

```bash
pip install -e .
```

## How to run the database service in the background

```bash
docker-compose -f service/run/mongo.yml up # Or 'up -d' to start in the background
```

## Q&A

- Where are stored the db files ? In a named docker data volume, see `service/run/*yml` file.

## How to cite

Please cite: Koch M, Duigou T, Faulon JL. Reinforcement Learning for Bioretrosynthesis. ACS Synthetic Biology (2020). DOI: https://doi.org/10.1021/acssynbio.9b00447

## License

See LICENSE file