# UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery

arXiv: https://arxiv.org/abs/2502.12453 · HTML: https://mlsanity.com/paper/2502.12453/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization](https://mlsanity.com/paper/2605.15824/) (2026)
- [AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery](https://mlsanity.com/paper/2505.11878/) (2025)
- [CalibHyper: Chance-Corrected Relational Hypergraphs for Few-Shot Molecular Property Prediction](https://mlsanity.com/paper/2609.33342/) (2026)
- [AssayRouter: Historical Utility Priors for Frozen Molecular Predictor Routing](https://mlsanity.com/paper/2609.37285/) (2026)

## Abstract (arXiv)

Drug discovery is crucial for identifying candidate drugs for various diseases.However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem. Existing methods primarily focus on single-scale features, overlooking the hierarchical molecular structures that determine different molecular properties. To address these issues, we introduce Universal Matching Networks (UniMatch), a dual matching framework that integrates explicit hierarchical molecular matching with implicit task-level matching via meta-learning, bridging multi-level molecular representations and task-level generalization. Specifically, our approach explicitly captures structural features across multiple levels, such as atoms, substructures, and molecules, via hierarchical pooling and matching, facilitating precise molecular representation and comparison. Additionally, we employ a meta-learning strategy for implicit task-level matching, allowing the model to capture shared patterns across tasks and quickly adapt to new ones. This unified matching framework ensures effective molecular alignment while leveraging shared meta-knowledge for fast adaptation. Our experimental results demonstrate that UniMatch outperforms state-of-the-art methods on the MoleculeNet and FS-Mol benchmarks, achieving improvements of 2.87% in AUROC and 6.52% in delta AUPRC. UniMatch also shows excellent generalization ability on the Meta-MolNet benchmark.