Multi-View Molecular Representation Learning with Hierarchical Graphs and Contextualized Fingerprints

• URL: http://arxiv.org/abs/2609.15611v1
• Date: Mon, 14 Sep 2026 14:07:19 GMT
• Title: Multi-View Molecular Representation Learning with Hierarchical Graphs and Contextualized Fingerprints
• Abstract summary: HiFi-Mol is a multi-view framework that pretrains a hierarchical graph encoder and a contextualized fingerprint encoder before downstream integration.<n>During fine-tuning, HiFi-Mol combines projected multi-resolution graph features with fingerprint embeddings for downstream prediction.
• Score: 2.7742696928124917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular property prediction requires representations that generalize from limited labeled data to structurally novel compounds. Existing molecular pretraining methods often rely on a single view: graph-based approaches model atom-bond topology but provide limited fragment-level supervision, whereas fingerprint descriptors encode chemical patterns but are typically used as fixed auxiliary features. We propose HiFi-Mol, a multi-view framework that separately pretrains a hierarchical graph encoder and a contextualized fingerprint encoder before downstream integration. The graph branch uses fragment-aware masking with multi-resolution supervision to capture substructure-aware representations, while the fingerprint branch tokenizes active entries from seven fingerprint families and applies masked language modeling to learn contextualized embeddings. During fine-tuning, HiFi-Mol combines projected multi-resolution graph features with fingerprint embeddings for downstream prediction. Evaluated on MoleculeNet benchmarks under the scaffold split, HiFi-Mol achieves a 2.77% improvement in average ROC-AUC over the best baseline across eight classification tasks while maintaining competitive performance on three regression tasks. Further analyses reveal that fragment-aware masking improves graph representation quality, and classification results demonstrate dataset-dependent strengths of the individual graph and fingerprint variants, confirming that the two views provide complementary predictive signals.

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