synrxn 0.0.5

Reaction Database for benchmarking

SynRXN

Reaction Database for Benchmarking SynRXN is a curated, provenance-tracked collection of reaction datasets and evaluation manifests designed for reproducible benchmarking of reaction-informatics tasks (rebalancing, atom-atom mapping, reaction classification, property prediction, and synthesis/retrosynthesis). It provides standardized splits, manifest files (RNG seeds & split indices), and lightweight utilities to load and inspect datasets for fair, reproducible model comparison.

Installation

1. Python Installation: Ensure that Python 3.11 or later is installed on your system. You can download it from python.org.

2. Creating a Virtual Environment (Optional but Recommended): It's recommended to use a virtual environment to avoid conflicts with other projects or system-wide packages. Use the following commands to create and activate a virtual environment:

python -m venv synrxn-env
source synrxn-env/bin/activate  

Or Conda

conda create --name synrxn-env python=3.11
conda activate synrxn-env

3. Install from PyPi: The easiest way to use SynTemp is by installing the PyPI package synrxn.

pip install synrxn

Optional if you want to install full version

pip install synrxn[all]

Example

from synrxn.data_loader import DataLoader
from synrxn.split.repeated_kfold import RepeatedKFoldsSplitter

# create a loader for the 'property' task and print available dataset names
dl = DataLoader(task="property")
dl.print_names()

# expected stdout:
# >>Datasets in task 'property': 11
#   b97xd3                          lograte                         rgd1                          
#   cycloadd                        phosphatase                     sn2                           
#   e2                              rad6re                          snar                          
#   e2sn2                

# load the 'b97xd3' dataset (returns a pandas.DataFrame)
df = dl.load("b97xd3")
splitter = RepeatedKFoldsSplitter(
    n_splits=5,
    n_repeats=5,
    ratio=(8, 1, 1),
    shuffle=True,
    random_state=42,
)

# compute splits (no stratification in this example)
splitter.split(df, stratify_col=None)

# retrieve one specific split (repeat 0, fold 0) as pandas DataFrames
train_df, val_df, test_df = splitter.get_split(repeat=0, fold=0, as_frame=True)

# quick checks
print(type(train_df), type(val_df), type(test_df))     
print(len(train_df), len(val_df), len(test_df))        
print(train_df.columns.tolist())          

Contributing

• Tieu-Long Phan

Publication

SynRXN: A Benchmarking Framework and Open Data Repository for Computer-Aided Synthesis Planning

License

This project is licensed under MIT License - see the License file for details.

Acknowledgments

This project has received funding from the European Unions Horizon Europe Doctoral Network programme under the Marie-Skłodowska-Curie grant agreement No 101072930 (TACsy -- Training Alliance for Computational)