MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model (2025.findings-emnlp)
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| Challenge: | Existing molecule-text models ignore complementary information in different molecular views and rely on single-view representations, limiting molecule structural understanding. |
| Approach: | They propose a framework that aligns multi-view molecular representations into a unified textual space using a multi-querying transformer. |
| Outcome: | The proposed framework ensures cross-view consistency while the proposed token-level contrastive loss preserves diverse molecular features across textual queries. |
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| Challenge: | Existing databases contain tens of millions of molecules; PubChem alone has 110 million compounds. |
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Veronika Ganeeva, Andrey Sakhovskiy, Kuzma Khrabrov, Andrey Savchenko, Artur Kadurin, Elena Tutubalina
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| Challenge: | Existing models lack the ability to learn fine-grained alignments between molecules and their descriptions. |
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| Challenge: | Recent advances in large language models (LLMs) have shown significant performance, approaching human perception levels. |
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| Challenge: | Language Models (LMs) have demonstrated impressive molecule understanding ability on 1D text-related tasks, but lack 2D graph perception, a critical ability of human professionals in comprehending molecules’ topological structures. |
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| Challenge: | Existing molecule-language models obscure the hierarchical organization of chemical semantics . Existing models rely on linear or uniform encodings, causing structural distortion . |
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| Challenge: | Large Language Models (LLMs) can attain professional-level proficiency in specific domains through fine-tuning. |
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