ReTReK: Retrosynthesis Planning Application using Retrosynthesis Knowledge (Python)

Welcome to the ReTReKpy project !!!

Environment

This version of the ReTReKpy repository is utilizing the kMoL library. The execution environment can be established using conda and pip as follows:

git clone https://github.com/elix-tech/kmol.git

cd kmol

make create-env

conda activate kmol

conda install -c conda-forge -c ljn917 molvs rdchiral_cpp -y

pip install epam.indigo quantulum3

cd /.../ReTReKpy

pip install --no-build-isolation -e . --user

NOTE: If this issue is encountered during the set-up of the environment, please add - mkl=2024.0 at the end of the /.../kmol/environment.yml file before running the make create-env command.

Docker

The execution environment can be established using Docker as follows:

git clone https://github.com/elix-tech/kmol.git

cd kmol

make build-docker

cd /.../ReTReKpy

docker build . -t retrekpy

alias retrekpy_docker='docker run --rm -it --gpus=all --ipc=host --volume="$(pwd)"/:/opt/elix/kmol/ReTReKpy/ retrekpy'

Now, the python command can be replaced with the retrekpy_docker command.

Scripts

Datasets

The following script is utilized to create the chemical reaction template dataset:

python -m scripts.datasets.create_reaction_template_dataset
  --input_file_path  # The path of the USPTO Grants or Applications '*.rsmi' file.
  --output_folder_path  # The path to the output directory.
  --random_seed  # The random seed value.
  --num_cores  # The number of CPU cores for multiprocessing.

The following script is utilized to create the chemical compound evaluation dataset:

python -m scripts.datasets.create_evaluation_dataset
  --input_file_path  # The path of any raw ChEMBL '*.txt' file.
  --output_folder_path  # The path to the output directory.
  --salts_file_path  # The path to the salts file.
  --min_atoms  # The minimum atoms cutoff value.
  --max_atoms  # The maximum atoms cutoff value.
  --fp_similarity_cutoff  # The fingerprint similarity cutoff value during Butina clustering.
  --small_dataset_size  # The size of the small evaluation dataset.
  --large_dataset_size  # The size of the large evaluation dataset.
  --outlier_percentage  # The percentage of outliers included in the final evaluation datasets.
  --random_seed  # The random seed value.
  --num_cores  # The number of CPU cores for multiprocessing.

The following script is utilized to create the additional information dataset:

python -m scripts.datasets.create_negative_reaction_dataset
  --input_folder_path  # The path of the USPTO Grants or Applications folder.
  --output_folder_path  # The path to the output directory.
  --random_seed  # The random seed value.
  --num_cores  # The number of CPU cores for multiprocessing.

The following script is utilized to create the in-scope filter training datasets:

python -m scripts.datasets.create_negative_reaction_dataset
  --input_ai_file_path  # The path of the additional information dataset file.
  --input_rt_file_path  # The path of the reaction template dataset file.
  --output_folder_path  # The path to the output directory.
  --yield_confidence  # The reaction yield confidence value.
  --yield_cutoff  # The reaction yield percentage cutoff value.
  --min_template_frequency  # The minimum reaction template frequency.
  --rt_virtual_negatives  # The number of virtual negative examples generated using random reaction templates.
  --rp_virtual_negatives  # The number of virtual negative examples generated using random perturbations.
  --random_seed  # The random seed value.
  --num_cores  # The number of CPU cores for multiprocessing.

Single-step Retrosynthesis

The following script is utilized to create the single-step retrosynthesis model dataset:

python -m scripts.single_step_retrosynthesis.create_dataset
  --dataset_file_path  # The path to the '*.csv' dataset file.
  --output_directory_path  # The path to the output directory.
  --minimum_number_of_reaction_template_occurrences # The minimum number of chemical reaction template occurrences.
  --number_of_cross_validation_splits  # The number of cross-validation splits.
  --validation_percentage  # The percentage of the dataset that should be utilized for validation.
  --random_seed  # The random seed value.

The following scripts are utilized to train, test, and infer the single-step retrosynthesis model:

python -m scripts.single_step_retrosynthesis.train
  --configuration_file_path  # The path to the configuration file.

python -m scripts.single_step_retrosynthesis.test
  --configuration_file_path  # The path to the configuration file.

python -m scripts.single_step_retrosynthesis.inference
  --compound_smiles  # The chemical compound SMILES string.
  --configuration_file_path  # The path to the configuration file.

In-scope Filter

The following script is utilized to train the in-scope filter model:

python -m scripts.in_scope_filter.train
  --csv_path  # The path to the '*.csv' dataset file.
  --reaction_column  # The name of the chemical reaction column from the '*.csv' dataset file.
  --product_column # The name of the chemical reaction product column from the '*.csv' dataset file.
  --label_column  # The name of the label column from the '*.csv' dataset file.
  --pos_weight  # The positive weight for the BinaryCrossEntropy loss calculation.
  --learning_rate  # The learning rate value.
  --weight_decay  # The weight decay value.
  --epochs  # The number of epochs.
  --batch_size  # The batch size.
  --num_workers  # The number number of workers utilized to loading the dataset. 
  --use_cuda  # The indicator of whether CUDA should be utilized.
  --save_path  # The path to the output directory.

MCTS

The following script is utilized to run the Monte Carlo Tree Search:

python -m scripts.mcts.run
  --target  # The path to the target chemical compound '*.mol' file.
  --save_result_dir  # The path to the output directory.
  --config  # The path to the configuration file.
  --debug  # The indicator of whether to enable the debug mode.
  --random_seed  # The random seed value.

python -m scripts.mcts.run_all
  --targets  # The path to the directory containing the target chemical compound '*.mol' files.
  --save_result_dir  # The path to the output directory.
  --config  # The path to the configuration file.
  --debug  # The indicator of whether to enable the debug mode.
  --random_seed  # The random seed value.

Ease-of-synthesis

The following script is utilized to create the ease-of-synthesis model dataset:

python -m scripts.ease_of_synthesis.create_dataset
  --data_folder  # The path to the directory containing the results of the ReTReK approach.
  --uspto_template_path  # The path to the '*.csv' dataset file used for the training of the single-step retrosynthesis model.
  --template_column  # The name of the forward chemical reaction template column from the '*.csv' dataset file.
  --output_path  # The output directory path where the newly-created dataset should be stored.

The following scripts are utilized to train and infer the ease-of-synthesis model:

python -m scripts.ease_of_synthesis.train
  --config  # The path to the configuration file.
  --task # The indicator of the task: "train", "eval", or "bayesian_opt". 
  --num_workers # The number number of workers utilized to loading the dataset. 
  --eval_output_path # The path to a custom directory to store the evaluation results.

python -m scripts.ease_of_synthesis.inference
  --classification_model  # The path to the binary classification model configuration file.
  --regression_model  # The path to the regression model configuration file.
  --data  # The chemical compound SMILES string or the path to a file containing one chemical compound SMILES string per each line.
  --save_path  # The output directory path where the results should be stored.
  --featurizer  # The indicator of the featurizer that should be utilized: "ecfp", "graph", or "mordred". 

Contact

If you have any additional questions or comments, please feel free to reach out via GitHub Issues or via e-mail:

• Shoichi Ishida: ishida.sho.nm@yokohama-cu.ac.jp
• Ryosuke Kojima: kojima.ryosuke.8e@kyoto-u.ac.jp

Citation

@article{
    Ishida2022,
    author = {Shoichi Ishida and Kei Terayama and Ryosuke Kojima and Kiyosei Takasu and Yasushi Okuno},
    title = {AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge},
    journal = {Journal of Chemical Information and Modeling},
    volume = {62},
    number = {6},
    pages = {1357-1367},
    year = {2022},
    doi = {https://doi.org/10.1021/acs.jcim.1c01074}
}