Multi-View Molecular Representation Learning with Hierarchical Graphs and Contextualized Fingerprints
cs.LG, cs.AI
Submitted: 2026-09-14
Updated: 2026-09-14
Comments: The paper has been accepted as a Full Research Paper at CIKM '26. Source code is available at https://github.com/knhc1234/HiFi-Mol
Code: https://github.com/knhc1234/HiFi-Mol
License: http://creativecommons.org/licenses/by/4.0/
The gist: Molecular property prediction requires representations that generalize from limited labeled data to structurally novel compounds.
Terminology
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.
Sources
- OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
- Strategies for Pre-training Graph Neural Networks
- Pre-training Molecular Graph Representation with 3D Geometry
- InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
- How Powerful are Graph Neural Networks?
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