Papers › Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval
Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval
Zijun Min, Bingshuai Liu, Liang Zhang, Jia Song, Jinsong Su, Song He, Xiaochen Bo
The field of bioinformatics has seen significant progress, making the cross-modal text-molecule retrieval task increasingly vital. This task focuses on accurately retrieving molecule structures based on textual descriptions, by effectively aligning textual descriptions and molecules to assist researchers in identifying suitable molecular candidates. However, many existing approaches overlook the details inherent in molecule sub-structures. In this work, we introduce the Optimal TRansport-based Multi-grained Alignments model (ORMA), a novel approach that facilitates multi-grained alignments between textual descriptions and molecules. Our model features a text encoder and a molecule encoder. The text encoder processes textual descriptions to generate both token-level and sentence-level representations, while molecules are modeled as hierarchical heterogeneous graphs, encompassing atom, motif, and molecule nodes to extract representations at these three levels. A key innovation in ORMA is the application of Optimal Transport (OT) to align tokens with motifs, creating multi-token representations that integrate multiple token alignments with their corresponding motifs. Additionally, we employ contrastive learning to refine cross-modal alignments at three distinct scales: token-atom, multitoken-motif, and sentence-molecule, ensuring that the similarities between correctly matched text-molecule pairs are maximized while those of unmatched pairs are minimized. To our knowledge, this is the first attempt to explore alignments at both the motif and multi-token levels. Experimental results on the ChEBI-20 and PCdes datasets demonstrate that ORMA significantly outperforms existing state-of-the-art (SOTA) models.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-Modal Retrieval | ChEBI-20 | ORMA | Hits@1 | 66.5 | #2 of 9 | Archive leaderboard | report |
| Cross-Modal Retrieval | ChEBI-20 | ORMA | Hits@10 | 93.9 | #2 of 9 | Archive leaderboard | report |
| Cross-Modal Retrieval | ChEBI-20 | ORMA | Mean Rank | 18.53 | #2 of 9 | Archive leaderboard | report |
| Cross-Modal Retrieval | ChEBI-20 | ORMA | Test MRR | 77.2 | #2 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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