Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916439918641152 |
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| author | Ma, Tengfei Lin, Xuan Li, Tianle Li, Chaoyi Chen, Long Zhou, Peng Cai, Xibao Yang, Xinyu Zeng, Daojian Cao, Dongsheng Zeng, Xiangxiang |
| author_facet | Ma, Tengfei Lin, Xuan Li, Tianle Li, Chaoyi Chen, Long Zhou, Peng Cai, Xibao Yang, Xinyu Zeng, Daojian Cao, Dongsheng Zeng, Xiangxiang |
| contents | Large Language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as drug development remains challenges. To solve these challenges, we introduce \textbf{Y-Mol}, forming a well-established LLM paradigm for the flow of drug development. Y-Mol is a multiscale biomedical knowledge-guided LLM designed to accomplish tasks across lead compound discovery, pre-clinic, and clinic prediction. By integrating millions of multiscale biomedical knowledge and using LLaMA2 as the base LLM, Y-Mol augments the reasoning capability in the biomedical domain by learning from a corpus of publications, knowledge graphs, and expert-designed synthetic data. The capability is further enriched with three types of drug-oriented instructions: description-based prompts from processed publications, semantic-based prompts for extracting associations from knowledge graphs, and template-based prompts for understanding expert knowledge from biomedical tools. Besides, Y-Mol offers a set of LLM paradigms that can autonomously execute the downstream tasks across the entire process of drug development, including virtual screening, drug design, pharmacological properties prediction, and drug-related interaction prediction. Our extensive evaluations of various biomedical sources demonstrate that Y-Mol significantly outperforms general-purpose LLMs in discovering lead compounds, predicting molecular properties, and identifying drug interaction events. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_11550 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development Ma, Tengfei Lin, Xuan Li, Tianle Li, Chaoyi Chen, Long Zhou, Peng Cai, Xibao Yang, Xinyu Zeng, Daojian Cao, Dongsheng Zeng, Xiangxiang Artificial Intelligence Computation and Language Large Language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as drug development remains challenges. To solve these challenges, we introduce \textbf{Y-Mol}, forming a well-established LLM paradigm for the flow of drug development. Y-Mol is a multiscale biomedical knowledge-guided LLM designed to accomplish tasks across lead compound discovery, pre-clinic, and clinic prediction. By integrating millions of multiscale biomedical knowledge and using LLaMA2 as the base LLM, Y-Mol augments the reasoning capability in the biomedical domain by learning from a corpus of publications, knowledge graphs, and expert-designed synthetic data. The capability is further enriched with three types of drug-oriented instructions: description-based prompts from processed publications, semantic-based prompts for extracting associations from knowledge graphs, and template-based prompts for understanding expert knowledge from biomedical tools. Besides, Y-Mol offers a set of LLM paradigms that can autonomously execute the downstream tasks across the entire process of drug development, including virtual screening, drug design, pharmacological properties prediction, and drug-related interaction prediction. Our extensive evaluations of various biomedical sources demonstrate that Y-Mol significantly outperforms general-purpose LLMs in discovering lead compounds, predicting molecular properties, and identifying drug interaction events. |
| title | Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2410.11550 |