metadata
license: mit
tags:
- chemistry
- molecular-property-prediction
- drug-discovery
datasets:
- natelgrw/ReTiNA
pipeline_tag: tabular-regression
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}},
}