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---
license: mit
tags:
- chemistry
- molecular-property-prediction
- drug-discovery
datasets:
- natelgrw/AMAX
pipeline_tag: tabular-regression
---
# AMAX Models: Molecular Absorption Wavelength Prediction
A collection of machine learning models for predicting maximum absorption wavelength (λ<sub>max</sub>) of chemical compounds in various solvents. These models use molecular descriptors to predict spectroscopic properties, useful for drug discovery, materials science, and computational chemistry applications.
Source code for the AMAX model collection is available at this [Github Repository](https://github.com/natelgrw/amax_models).
The AMAX dataset is available at this [Hugging Face Repository](https://huggingface.co/datasets/natelgrw/AMAX).
## 🤖 Available Models
| Model | Architecture | Overall RMSE (nm) | Overall MAE (nm) | Overall R<sup>2</sup> |
|-----|-----|-----|-----|-----|
| **AMAX_XGB1** | XGBoost | 56.488 | 36.005 | 0.746 |
| **AMAX_MLP1** | PyTorch Sequential MLP | 64.152 | 44.388 | 0.669 |
All models were evaluated across rigorous scaffold, cluster, and method splits.
## 📄 Citation
If you use an AMAX prediction model in your research, please cite:
```bibtex
@modelcollection{amaxmodels,
title={AMAX-Models: Machine Learning Models for Molecular Absorption Wavelength Prediction},
author={Leung, Nathan},
institution={Coley Research Group @ MIT}
year={2025},
howpublished={\url{https://huggingface.co/natelgrw/AMAX-Models}},
}
``` |