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 (λmax) prediction and LC-MS compound characterization workflows.
Available models trained on the AMAX dataset are available at this Hugging Face Repository.
Source code for the AMAX model collection is available at this Github Repository.
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 λmax 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}}
}