Multi-View Molecular Pretraining Combines Hierarchical Graphs With Contextualized Fingerprints

A new arXiv paper proposes a molecular representation learning approach that combines multiple views rather than relying on a single one. It pairs hierarchical graph modeling of atom-bond topology with contextualized molecular fingerprints to improve property prediction. The goal is representations that generalize from limited labeled data to structurally novel compounds.