natelgrw/AMAX-Models

AMAX Models: Molecular Absorption Wavelength Prediction

A collection of machine learning models for predicting maximum absorption wavelength (λ_max) 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.

🤖 Available Models

Model	Framework	Architecture	R² Score	MAE (nm)	RMSE (nm)	Status
AMAX_XGB1	XGBoost	Gradient Boosting (500 estimators)	0.9084	17.682	35.507	Active
AMAX_RF1	Scikit-Learn	Random Forest (500 trees)	0.9035	18.601	36.441	Active
AMAX_MLP1	PyTorch	Sequential NN (1024 → 512)	0.8913	23.956	38.680	Active

All models utilize 312 RDKit molecular descriptors combining both compound and solvent features, trained on a random data split of 32,010 training samples with 4,001 validation and 4,002 test samples. Each model has been retrained to eliminate data leakage and ensure robust performance evaluation.

📄 Citation

If you use an AMAX prediction model in your research, please cite:

@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}},
}