Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866916384894615552 |
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| author | Zheng, Yizhen Koh, Huan Yee Yang, Maddie Li, Li May, Lauren T. Webb, Geoffrey I. Pan, Shirui Church, George |
| author_facet | Zheng, Yizhen Koh, Huan Yee Yang, Maddie Li, Li May, Lauren T. Webb, Geoffrey I. Pan, Shirui Church, George |
| contents | The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes. This review highlights the expanding role of LLMs in revolutionizing various stages of the drug development pipeline. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes. Our paper aims to provide a comprehensive overview for researchers and practitioners in computational biology, pharmacology, and AI4Science by offering insights into the potential transformative impact of LLMs on drug discovery and development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04481 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials Zheng, Yizhen Koh, Huan Yee Yang, Maddie Li, Li May, Lauren T. Webb, Geoffrey I. Pan, Shirui Church, George Quantitative Methods Artificial Intelligence Machine Learning The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes. This review highlights the expanding role of LLMs in revolutionizing various stages of the drug development pipeline. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes. Our paper aims to provide a comprehensive overview for researchers and practitioners in computational biology, pharmacology, and AI4Science by offering insights into the potential transformative impact of LLMs on drug discovery and development. |
| title | Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.04481 |