README.md

---
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}}
}
```