Unlocking the Capabilities of Large Language Models for Accelerating Drug Development

被引:1
|
作者
Anderson, Wes [1 ]
Braun, Ian [1 ]
Bhatnagar, Roopal [1 ]
Romero, Klaus [1 ]
Walls, Ramona [1 ]
Schito, Marco [1 ]
Podichetty, Jagdeep T. [1 ]
机构
[1] Crit Path Inst, Tucson, AZ 85718 USA
关键词
DIGITAL THERAPEUTICS; HEALTH; FUTURE;
D O I
10.1002/cpt.3279
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Recent breakthroughs in natural language processing (NLP), particularly in large language models (LLMs), offer substantial advantages in model-informed drug development (MIDD). With billions of parameters and comprehensive pre-training on diverse data, these models effectively extract information from unstructured and structured data throughout the drug development lifecycle. This perspective envisions LLMs supporting MIDD, enhancing drug development, and emphasizes C-Path's strategic use of LLM innovations for actionable real-world evidence from real-world data (RWD).
引用
收藏
页码:38 / 41
页数:4
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