Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914172736897024 |
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| author | Pei, Qizhi Zhou, Zhimeng Gao, Kaiyuan Zhu, Jinhua Wang, Yue Wang, Zun Qin, Tao Wu, Lijun Yan, Rui |
| author_facet | Pei, Qizhi Zhou, Zhimeng Gao, Kaiyuan Zhu, Jinhua Wang, Yue Wang, Zun Qin, Tao Wu, Lijun Yan, Rui |
| contents | The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. This approach leverages the rich, multifaceted descriptions of biomolecules contained within textual data sources to enhance our fundamental understanding and enable downstream computational tasks such as biomolecule property prediction. The fusion of the nuanced narratives expressed through natural language with the structural and functional specifics of biomolecules described via various molecular modeling techniques opens new avenues for comprehensively representing and analyzing biomolecules. By incorporating the contextual language data that surrounds biomolecules into their modeling, BL aims to capture a holistic view encompassing both the symbolic qualities conveyed through language as well as quantitative structural characteristics. In this review, we provide an extensive analysis of recent advancements achieved through cross modeling of biomolecules and natural language. (1) We begin by outlining the technical representations of biomolecules employed, including sequences, 2D graphs, and 3D structures. (2) We then examine in depth the rationale and key objectives underlying effective multi-modal integration of language and molecular data sources. (3) We subsequently survey the practical applications enabled to date in this developing research area. (4) We also compile and summarize the available resources and datasets to facilitate future work. (5) Looking ahead, we identify several promising research directions worthy of further exploration and investment to continue advancing the field. The related resources and contents are updating in https://github.com/QizhiPei/Awesome-Biomolecule-Language-Cross-Modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01528 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey Pei, Qizhi Zhou, Zhimeng Gao, Kaiyuan Zhu, Jinhua Wang, Yue Wang, Zun Qin, Tao Wu, Lijun Yan, Rui Computation and Language Artificial Intelligence Biomolecules The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. This approach leverages the rich, multifaceted descriptions of biomolecules contained within textual data sources to enhance our fundamental understanding and enable downstream computational tasks such as biomolecule property prediction. The fusion of the nuanced narratives expressed through natural language with the structural and functional specifics of biomolecules described via various molecular modeling techniques opens new avenues for comprehensively representing and analyzing biomolecules. By incorporating the contextual language data that surrounds biomolecules into their modeling, BL aims to capture a holistic view encompassing both the symbolic qualities conveyed through language as well as quantitative structural characteristics. In this review, we provide an extensive analysis of recent advancements achieved through cross modeling of biomolecules and natural language. (1) We begin by outlining the technical representations of biomolecules employed, including sequences, 2D graphs, and 3D structures. (2) We then examine in depth the rationale and key objectives underlying effective multi-modal integration of language and molecular data sources. (3) We subsequently survey the practical applications enabled to date in this developing research area. (4) We also compile and summarize the available resources and datasets to facilitate future work. (5) Looking ahead, we identify several promising research directions worthy of further exploration and investment to continue advancing the field. The related resources and contents are updating in https://github.com/QizhiPei/Awesome-Biomolecule-Language-Cross-Modeling. |
| title | Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey |
| topic | Computation and Language Artificial Intelligence Biomolecules |
| url | https://arxiv.org/abs/2403.01528 |