MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910173468033024 |
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| author | Li, Jiatong Liu, Yunqing Liu, Wei Le, Jingdi Zhang, Di Fan, Wenqi Zhou, Dongzhan Li, Yuqiang Li, Qing |
| author_facet | Li, Jiatong Liu, Yunqing Liu, Wei Le, Jingdi Zhang, Di Fan, Wenqi Zhou, Dongzhan Li, Yuqiang Li, Qing |
| contents | Molecule discovery is a pivotal research field, impacting everything from medicine to materials. Recently, Large Language Models (LLMs) have been widely adopted in molecular understanding and generation, serving as a bridge between the molecular space and the natural language space, yet the alignment between molecules and their corresponding captions remains a significant challenge. Previous endeavors typically treat molecules as monolithic inputs, lacking an intermediate reasoning process and sacrificing explainability. In this work, we define fine-grained alignments as the precise correspondence between a molecule's sub-structures and the textual phrases that explain their properties. These alignments are crucial for LLMs to understand molecules in a more accurate and explainable manner. Normally, such fine-grained alignments require expert annotation, which is both costly and time-consuming. To allow LLMs to automatically label and learn the fine-grained alignments, we propose MolReFlect, a novel teacher-student framework, where a teacher LLM first generates and refines mappings between caption phrases and SMILES substructures and then explicitly teaches these detailed alignments to a student LLM. Experimental results demonstrate that MolReFlect enables LLMs to significantly outperform previous baselines, achieving the state-of-the-art performance in the molecule-caption translation task. Our codes are available via: https://github.com/phenixace/MolReFlect. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_14721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts Li, Jiatong Liu, Yunqing Liu, Wei Le, Jingdi Zhang, Di Fan, Wenqi Zhou, Dongzhan Li, Yuqiang Li, Qing Computation and Language Machine Learning Quantitative Methods Molecule discovery is a pivotal research field, impacting everything from medicine to materials. Recently, Large Language Models (LLMs) have been widely adopted in molecular understanding and generation, serving as a bridge between the molecular space and the natural language space, yet the alignment between molecules and their corresponding captions remains a significant challenge. Previous endeavors typically treat molecules as monolithic inputs, lacking an intermediate reasoning process and sacrificing explainability. In this work, we define fine-grained alignments as the precise correspondence between a molecule's sub-structures and the textual phrases that explain their properties. These alignments are crucial for LLMs to understand molecules in a more accurate and explainable manner. Normally, such fine-grained alignments require expert annotation, which is both costly and time-consuming. To allow LLMs to automatically label and learn the fine-grained alignments, we propose MolReFlect, a novel teacher-student framework, where a teacher LLM first generates and refines mappings between caption phrases and SMILES substructures and then explicitly teaches these detailed alignments to a student LLM. Experimental results demonstrate that MolReFlect enables LLMs to significantly outperform previous baselines, achieving the state-of-the-art performance in the molecule-caption translation task. Our codes are available via: https://github.com/phenixace/MolReFlect. |
| title | MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts |
| topic | Computation and Language Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2411.14721 |