Summary of Leveraging Biomolecule and Natural Language Through Multi-modal Learning: a Survey, by Qizhi Pei et al.
Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
by Qizhi Pei, Lijun Wu, Kaiyuan Gao, Jinhua Zhu, Yue Wang, Zun Wang, Tao Qin, Rui Yan
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The integration of biomolecular modeling with natural language has emerged as a promising interdisciplinary area, leveraging rich descriptions of biomolecules in textual data sources to enhance understanding and enable computational tasks like biomolecule property prediction. The fusion of nuanced narratives with structural and functional specifics of biomolecules opens new avenues for comprehensively representing and analyzing biomolecules. By incorporating contextual language data into molecular modeling, BL aims to capture a holistic view encompassing both symbolic and quantitative characteristics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Biomolecular modeling with natural language (BL) combines artificial intelligence, chemistry, and biology to better understand and predict the properties of biomolecules. This approach uses text-based descriptions of biomolecules from various sources to improve our understanding of their structure and function. By combining these textual data with molecular models, BL aims to provide a more comprehensive view of biomolecules. |
