natelgrw/ReTiNA-Models

ReTiNA Models: Molecular Retention Time Prediction

A collection of machine learning models for predicting the retention time of chemical compounds in various LC-MS. These models use molecular descriptors and method encodings to predict chemical retention times, useful for automated compound identification.

Source code for the ReTiNA model collection is available at this Github Repository.

The ReTiNA dataset is available at this Hugging Face Repository.

🤖 Available Models

In retention time prediction, we recommend using ReTiNA_XGB1, as it has the highest overall prediction accuracy.

Model	Architecture	Overall RMSE (s)	Overall MAE (s)	Overall R2
ReTiNA_XGB1	XGBoost	182.81	119.30	0.659
ReTiNA_MLP1	PyTorch Residual MLP	202.67	141.79	0.516

All models were evaluated across rigorous scaffold, cluster, and method splits.

📄 Citation

If you use a ReTiNA prediction model in your research, please cite:

@modelcollection{retinamodels,
  title={ReTiNA-Models: Machine Learning Models for LC-MS Retention Time Prediction},
  author={Leung, Nathan},
  institution={Coley Research Group @ MIT}
  year={2025},
  howpublished={\url{https://huggingface.co/natelgrw/ReTiNA-Models}},
}