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

来源：

CLINICAL PHARMACOLOGY & THERAPEUTICS | 2024年 / 116卷 / 01期

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

页码：38 / 41

页数：4

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