敏感癌症GPT:利用结构化Omics数据上的生成大型语言模型来优化药物敏感性预测.
Shaika Chowdhury1, Sivaraman Rajaganapathy1, Lichao Sun1,2
1Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN.
bioRxiv : the preprint server for biology
|March 10, 2025
概括
生成型大语言模型 (LLM) 在精确瘤学的药物敏感性预测 (DSP) 中表现有前途. 精细调整的GPT模型与即时工程显著提高了跨不同癌症细胞系的DSP性能.
科学领域:
- 药物基因组学 药物基因组学
- 计算生物学 计算生物学
- 在瘤学中使用人工智能
背景情况:
- 庞大的药物基因组学数据为精确瘤学中药物敏感性预测 (DSP) 提供了机会.
- 生成型大语言模型 (LLM) 显示出潜力,但在结构化药物基因组学数据方面存在困难.
研究的目的:
- 适应LLM的快速工程,以优化结构化药物基因组学数据上的DSP性能.
- 在现实世界DSP场景中评估LLM概括.
- 将LLM DSP的表现与最先进的科学基线进行比较.
主要方法:
- 在五种癌症组织类型的四个药物基因组学数据集上系统地研究生成预训练变压器 (GPT).
- 采用了具有指令,指令前和cloze模板的新提示工程,整合了药物基因组学特征.
- 通过零射击,少数射击,微调和集群预训练嵌入来评估GPT.
主要成果:
- 微调GPT实现了最好的DSP性能 (28%的F1增长),超过了几次拍摄的学习.
- 快速工程,特别是指令前模板,提高了F1的性能22%.
- 与基线和可比的跨组织概括相比,GPT显示出高于平均F1性能 (16%的增长).
结论:
- 像GPT这样的生成LLM是可行的in silico工具,用于指导精确瘤学.
- 优化的提示工程对于利用LLM与结构化药物基因组学数据至关重要.
- GPT的表现突显了其在药物敏感性预测和个性化癌症治疗方面的潜力.
相关概念视频
Drug Discovery: Overview
7.3K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
7.3K
Structure-Activity Relationships and Drug Design
474
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
474
Analysis of Population Pharmacokinetic Data
211
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
211


