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README.md

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 ---
 license: mit
+tags:
+- chemistry
+- molecular-property-prediction
+- drug-discovery
+datasets:
+- natelgrw/AMAX
+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
+
+| 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}},
+}
+```

models/ReTiNA_MLP1/ReTINA_MLP1.pt

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models/ReTiNA_XGB1/ReTINA_XGB1.json

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