DDI-GPT:使用大语言模型增强知识图的药物相互作用的可解释预测
bioRxiv : the preprint server for biology
|December 23, 2024
概括
DDI-GPT是一个新的深度学习框架,通过整合知识图和大型语言模型,准确地预测药物相互作用 (DDI). 该工具通过早期检测潜在的相互作用来提高药物安全性,优于现有的方法.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 人工智能在医学中的应用
- 生物信息学是一种生物信息学.
背景情况:
- 药物相互作用 (DDI) 对患者安全构成重大风险,并对药物开发构成挑战.
- 早期识别潜在的DDI对于减轻不良事件和改善治疗结果至关重要.
研究的目的:
- 引入DDI-GPT,这是一个用于预测药物相互作用 (DDI) 的深度学习框架.
- 通过知识图表和大型语言模型,加强对潜在药物相互作用的早期检测.
- 为药物安全性评估提供可解释的深度学习工具.
主要方法:
- 开发了DDI-GPT,这是一个结合知识图 (KG) 和预训练的大型语言模型 (LLM) 的框架.
- 利用特征归属方法用于途径和互动组网络上的可解释深度学习 (DL) 模型.
- 在TwoSIDES基准数据集上验证了性能,并应用于FDA不良事件报告系统数据以进行零射击预测.
主要成果:
- 在TwoSIDES数据集上,DDI-GPT实现了0.964的AUROC,超过了现有的DL方法.
- 在9,480个DDI记录中,在零射击预测中显示了0.84 AUROC,比以前最好的方法有14%的改进.
- 发现CYP3A丰富的信号,用于布鲁顿的氨酸激酶 (BTK) 抑制剂的毒性,提供机械洞察力.
结论:
- DDI-GPT有效地预测了DDI,在计算药物安全方面取得了重大进展.
- 该框架为DDI机制提供了可解释的见解,有助于理解药物毒性.
- DDI-GPT可以作为一个Web服务器和软件包,支持药物开发和临床安全.
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