使用大型语言模型在药物分子和指示之间进行翻译的新兴机会
David Oniani1, Jordan Hilsman1, Chengxi Zang2,3
1Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA.
Scientific reports
|May 10, 2024
概括
大型语言模型 (LLM) 被探索用于将药物分子转化为指示,反之亦然. 这种人工智能方法旨在通过实现向治疗来加速药物发现和降低成本.
科学领域:
- 人工智能的人工智能
- 药物发现 药物发现 药物发现
- 计算化学的计算化学
背景情况:
- 药物分子改变生理状态;指示定义了它们的治疗用途.
- 大型语言模型 (LLM) 在基于文本的分子描述方面表现出色,但它们用于分子指示翻译的用途尚未得到充分探索.
- 弥合这个差距可以大大推动药物发现.
研究的目的:
- 引入和评估用于药物分子及其相应指示之间的翻译的LLM.
- 评估T5 LLM各种配置在这个新任务上的表现.
- 提供有关当前最先进技术和未来方向的见解.
主要方法:
- 提出了一项新任务:药物分子与适应症之间的翻译.
- 测试了T5 LLM的九种变体.
- 评估了来自ChEMBL和DrugBank的数据集的性能.
主要成果:
- 介绍了关于分子指示翻译的LLM性能的初步发现.
- 为此任务提供了当前最先进的状态的视角.
- 突出了现有的局限性和未来改进的领域.
结论:
- 通过LLM驱动的分子指示翻译显示出药物发现的前景.
- 这种人工智能能力可以带来更有效的疾病向和降低开发成本.
- 在生成性AI时代,有可能彻底改变药物发现.
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