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---
license: mit
configs:
- config_name: default
data_files: ["data/amax_dataset.csv"]
---
## 〰️ AMAX: A Benchmark Dataset for UV-Vis Lambda Max Prediction in LC-MS
AMAX is an open source dataset designed to assist machine learning models in small molecule UV-Vis absorption maxima (λ<sub>max</sub>) prediction and LC-MS compound characterization workflows.
Available models trained on the AMAX dataset are available at this [Hugging Face Repository](https://huggingface.co/natelgrw/AMAX-Models).
Source code for the AMAX model collection is available at this [Github Repository](https://github.com/natelgrw/amax_models).
This dataset is actively expanding with new experimental retention time values from the Coley Research Group at MIT, ensuring it remains a growing resource for optical property prediction.
AMAX is designed for use in:
- Estimating retention times for new compound–environment combinations
- Aiding in peak assignment in LC-MS method development
- Training ML models for retention time prediction under specific conditions
## 📈 The AMAX Dataset
The AMAX dataset contains:
- 40,013 unique molecule–environment combinations, the largest singular LC-MS retention time dataset of its kind to date
- Experimentally measured λ<sub>max</sub> values in nm, curated from public datasets, benchmark papers, and literature
- 157 calculated chemical descriptors for 22,415 unique compounds and 356 unique solvents
Additionally, the AMAX dataset is divided into different scaffold, cluster, and solvent splits for model evaluation.
## 📋 Data Sources Used
Detailed information on the data sources comprising AMAX-1 can be found in the data folder.
## ✒️ Citation
If you use this code in a project, please cite the following:
```
@dataset{amaxdataset,
title={AMAX: A Benchmark Dataset for UV-Vis Lambda Max Prediction in LC-MS},
author={Leung, Nathan},
institution={Coley Research Group @ MIT}
year={2025},
howpublished={\url{https://huggingface.co/datasets/natelgrw/AMAX}}
}
``` |