Weighted Views Representation with Multi-Head Coulomb Attention for Molecular Property Prediction

We extend the weighted-views molecular featurization framework (d'Avezac, Botts, Mohlenkamp, and Zunger, 2011) by enriching it with single-atom properties and Coulomb pair information. We use these enriched views within a machine-learning architecture incorporating shared Transformers whose attention scores directly incorporate Coulomb interactions, weighted aggregation of view outputs, and a final prediction neural network. We demonstrate performance competitive with the state-of-the-art on the QM7 atomization energy benchmark (regression), ranking enantiomers by their docking scores (trained as regression, tested by ranking), lipophilicity (regression), and blood-brain barrier penetration (classification).