Papers › Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment
Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment
Jia Song, Wanru Zhuang, Yujie Lin, Liang Zhang, Chunyan Li, Jinsong Su, Song He, Xiaochen Bo
Cross-modal text-molecule retrieval model aims to learn a shared feature space of the text and molecule modalities for accurate similarity calculation, which facilitates the rapid screening of molecules with specific properties and activities in drug design. However, previous works have two main defects. First, they are inadequate in capturing modality-shared features considering the significant gap between text sequences and molecule graphs. Second, they mainly rely on contrastive learning and adversarial training for cross-modality alignment, both of which mainly focus on the first-order similarity, ignoring the second-order similarity that can capture more structural information in the embedding space. To address these issues, we propose a novel cross-modal text-molecule retrieval model with two-fold improvements. Specifically, on the top of two modality-specific encoders, we stack a memory bank based feature projector that contain learnable memory vectors to extract modality-shared features better. More importantly, during the model training, we calculate four kinds of similarity distributions (text-to-text, text-to-molecule, molecule-to-molecule, and molecule-to-text similarity distributions) for each instance, and then minimize the distance between these similarity distributions (namely second-order similarity losses) to enhance cross-modal alignment. Experimental results and analysis strongly demonstrate the effectiveness of our model. Particularly, our model achieves SOTA performance, outperforming the previously-reported best result by 6.4%.
Code
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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 | Song et al. | Hits@1 | 56.5 | #3 of 9 | Archive leaderboard | report |
| Cross-Modal Retrieval | ChEBI-20 | Song et al. | Hits@10 | 94.1 | #3 of 9 | Archive leaderboard | report |
| Cross-Modal Retrieval | ChEBI-20 | Song et al. | Mean Rank | 12.66 | #3 of 9 | Archive leaderboard | report |
| Cross-Modal Retrieval | ChEBI-20 | Song et al. | Test MRR | 70.2 | #3 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.
Methods
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