Jun 2026
Evaluating Molecular Representations for Predicting Cyclodextrin-PFAS Binding Energy with Machine Learning: Domain Transfer and Data Limitations.
This study systematically compares molecular representations (Mordred, ECFP, ChemBERTa, UniMol2, etc.) across several machine learning architectures to predict CD-PFAS binding energies, demonstrating that molecular representation choice is critical for small-data host-guest binding prediction.
Cole Brzakala, O. Moultos, J. P. van der Hoek et al.
· Journal of Chemical Informat... · 1 citation