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.
Current Version: 1.0.0
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}},
}