Advancing biomolecular understanding and design following human instructions
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911076562501632 |
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| author | Zhuang, Xiang Ding, Keyan Lyu, Tianwen Jiang, Yinuo Li, Xiaotong Xiang, Zhuoyi Wang, Zeyuan Qin, Ming Feng, Kehua Wang, Jike Zhang, Qiang Chen, Huajun |
| author_facet | Zhuang, Xiang Ding, Keyan Lyu, Tianwen Jiang, Yinuo Li, Xiaotong Xiang, Zhuoyi Wang, Zeyuan Qin, Ming Feng, Kehua Wang, Jike Zhang, Qiang Chen, Huajun |
| contents | Understanding and designing biomolecules, such as proteins and small molecules, is central to advancing drug discovery, synthetic biology and enzyme engineering. Recent breakthroughs in artificial intelligence have revolutionized biomolecular research, achieving remarkable accuracy in biomolecular prediction and design. However, a critical gap remains between artificial intelligence's computational capabilities and researchers' intuitive goals, particularly in using natural language to bridge complex tasks with human intentions. Large language models have shown potential to interpret human intentions, yet their application to biomolecular research remains nascent due to challenges including specialized knowledge requirements, multimodal data integration, and semantic alignment between natural language and biomolecules. To address these limitations, we present InstructBioMol, a large language model designed to bridge natural language and biomolecules through a comprehensive any-to-any alignment of natural language, molecules and proteins. This model can integrate multimodal biomolecules as the input, and enable researchers to articulate design goals in natural language, providing biomolecular outputs that meet precise biological needs. Experimental results demonstrate that InstructBioMol can understand and design biomolecules following human instructions. In particular, it can generate drug molecules with a 10% improvement in binding affinity and design enzymes that achieve an enzyme-substrate pair prediction score of 70.4. This highlights its potential to transform real-world biomolecular research. The code is available at https://github.com/HICAI-ZJU/InstructBioMol. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07919 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing biomolecular understanding and design following human instructions Zhuang, Xiang Ding, Keyan Lyu, Tianwen Jiang, Yinuo Li, Xiaotong Xiang, Zhuoyi Wang, Zeyuan Qin, Ming Feng, Kehua Wang, Jike Zhang, Qiang Chen, Huajun Computation and Language Biomolecules Understanding and designing biomolecules, such as proteins and small molecules, is central to advancing drug discovery, synthetic biology and enzyme engineering. Recent breakthroughs in artificial intelligence have revolutionized biomolecular research, achieving remarkable accuracy in biomolecular prediction and design. However, a critical gap remains between artificial intelligence's computational capabilities and researchers' intuitive goals, particularly in using natural language to bridge complex tasks with human intentions. Large language models have shown potential to interpret human intentions, yet their application to biomolecular research remains nascent due to challenges including specialized knowledge requirements, multimodal data integration, and semantic alignment between natural language and biomolecules. To address these limitations, we present InstructBioMol, a large language model designed to bridge natural language and biomolecules through a comprehensive any-to-any alignment of natural language, molecules and proteins. This model can integrate multimodal biomolecules as the input, and enable researchers to articulate design goals in natural language, providing biomolecular outputs that meet precise biological needs. Experimental results demonstrate that InstructBioMol can understand and design biomolecules following human instructions. In particular, it can generate drug molecules with a 10% improvement in binding affinity and design enzymes that achieve an enzyme-substrate pair prediction score of 70.4. This highlights its potential to transform real-world biomolecular research. The code is available at https://github.com/HICAI-ZJU/InstructBioMol. |
| title | Advancing biomolecular understanding and design following human instructions |
| topic | Computation and Language Biomolecules |
| url | https://arxiv.org/abs/2410.07919 |