readretro-test 1.0.1

READRetro lib test

READRetro: Natural Product Biosynthesis Planning with Retrieval-Augmented Dual-View Retrosynthesis

This is the official code repository for the paper READRetro: Natural Product Biosynthesis Planning with Retrieval-Augmented Dual-View Retrosynthesis (bioRxiv, 2023).
We also provide a web version for ease of use.

Installation

Run the following commands to install the dependencies:

conda create -n readretro python=3.8
conda activate readretro
conda install pytorch==1.12.0 cudatoolkit=11.3 -c pytorch
pip install easydict pandas tqdm numpy==1.22 OpenNMT-py==2.3.0 networkx==2.5    # need to fix the miner version of numpy (np.bool was deprecated)
conda install -c conda-forge rdkit=2019.09

Model Preparation

We provide the trained models in Google Drive.
Place the checkpoints under the folders g2s/saved_models and retroformer/saved_models.
You can use your own models trained using the official codes (https://github.com/coleygroup/Graph2SMILES and https://github.com/yuewan2/Retroformer).

Single-step Planning and Evaluation

Run the following command to evaluate the single-step performance of the models:

CUDA_VISIBLE_DEVICES=${gpu_id} python eval_single.py                    # ensemble
CUDA_VISIBLE_DEVICES=${gpu_id} python eval_single.py -m retroformer     # Retroformer
CUDA_VISIBLE_DEVICES=${gpu_id} python eval_single.py -m g2s -s 200      # Graph2SMILES

Multi-step Planning

Run the following command to plan paths of multiple products using multiprocessing:

CUDA_VISIBLE_DEVICES=${gpu_id} python run_mp.py
# e.g., CUDA_VISIBLE_DEVICES=0 python run_mp.py

You can modify other hyperparameters described in run_mp.py.
Lower num_threads if you run out of GPU capacity.

Run the following command to plan the retrosynthesis path of your own molecule:

CUDA_VISIBLE_DEVICES=${gpu_id} python run.py ${product}
# e.g., CUDA_VISIBLE_DEVICES=0 python run.py 'O=C1C=C2C=CC(O)CC2O1'

You can modify other hyperparameters described in run.py.

Multi-step Evaluation

Run the following command to evaluate the planned paths of the test molecules:

python eval.py ${save_file}
# e.g., python eval.py result/debug.txt