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生物医学生成预训练的基于变压器语言模型,用于与年龄相关的疾病目标发现

Diana Zagirova¹, Stefan Pushkov¹, Geoffrey Ho Duen Leung¹

¹Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China.

|September 24, 2023

概括

这项研究引入了一种新的AI方法,使用大型语言模型 (LLM) 来预测治疗目标. 该方法成功地确定了潜在的衰老和疾病目标,包括新型候选者,如CCR5和PTH.

科学领域:

生物医学研究的研究.
人工智能在药物发现中的作用

背景情况:

目标发现对于新疗法至关重要,但目前的方法有局限性.
自然语言处理 (NLP) 提供了预测治疗目标的新方法.

研究的目的:

开发和评估一种使用大型语言模型 (LLM) 预测治疗目标的新方法.
确定衰老和与年龄有关的疾病的潜在目标.

主要方法:

在生物医学文献 (赠款文本) 上训练了一个特定领域的BioGPT模型.
开发了一个用于生成治疗目标预测的管道.
根据现有数据库数据验证预测.

主要成果:

预先培训的LLM与特定任务的文本提高了绩效.
确定已知的衰老和与年龄相关的疾病目标.
建议CCR5和PTH作为新的双重目的抗衰老和疾病目标.

结论:

变压器模型显示了新的目标预测的巨大潜力.
人工智能集成为解决复杂的生物医学挑战提供了路线图.

关键词:

老化的生物标志物深度学习是一种深度学习.人类的衰老人类的衰老发现治疗目标的发现.变压器变压器

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