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--- |
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license: mit |
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tags: |
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- chemistry |
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- molecular-property-prediction |
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- drug-discovery |
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datasets: |
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- natelgrw/ReTiNA |
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pipeline_tag: tabular-regression |
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--- |
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# ReTiNA Models: Molecular Retention Time Prediction |
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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. |
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Source code for the ReTiNA model collection is available at this [Github Repository](https://github.com/natelgrw/retina_models). |
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The ReTiNA dataset is available at this [Hugging Face Repository](https://huggingface.co/datasets/natelgrw/ReTiNA). |
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## 🤖 Available Models |
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In retention time prediction, we recommend using **ReTiNA_XGB1**, as it has the highest overall prediction accuracy. |
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| Model | Architecture | Overall RMSE (s) | Overall MAE (s) | Overall R<sup>2</sup> | |
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|-----|-----|-----|-----|-----| |
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| **ReTiNA_XGB1** | XGBoost | 182.81 | 119.30 | 0.659 | |
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| **ReTiNA_MLP1** | PyTorch Residual MLP | 202.67 | 141.79 | 0.516 | |
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All models were evaluated across rigorous scaffold, cluster, and method splits. |
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## 📄 Citation |
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If you use a ReTiNA prediction model in your research, please cite: |
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``` |
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@modelcollection{retinamodels, |
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title={ReTiNA-Models: Machine Learning Models for LC-MS Retention Time Prediction}, |
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author={Leung, Nathan}, |
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institution={Coley Research Group @ MIT} |
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year={2025}, |
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howpublished={\url{https://huggingface.co/natelgrw/ReTiNA-Models}}, |
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} |
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``` |
