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---
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](https://github.com/natelgrw/retina_models).
The ReTiNA dataset is available at this [Hugging Face Repository](https://huggingface.co/datasets/natelgrw/ReTiNA).
## 🤖 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 R<sup>2</sup> |
|-----|-----|-----|-----|-----|
| **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}},
}
``` |