深度搜索-LLM与适应RAG用于制药溶解预测.
Leqi Lin1, Xingyu Zhou1, Kaiyuan Yang1
1State Key Laboratory of Synergistic Chem-Bio Synthesis, Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
Pharmaceutical research
|October 8, 2025
概括
这项研究使用先进的AI,包括大型语言模型 (LLM) 和扩散模型,来预测药物溶解概况. 这种方法通过减少对长时间实验的需求和将物理性质与微观结构连接起来来加速药物开发.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 人工智能在药物开发中的作用
背景情况:
- 传统的药物溶解预测在很大程度上依赖于耗时和昂贵的经验实验.
- 集成先进的计算方法可以显著加速药物开发管道.
- 生成型人工智能为模拟复杂的制药过程提供了新的方法.
研究的目的:
- 通过使用大型语言模型 (LLM) 和人工智能扩散模型加速和增强制药药物溶解预测.
- 在药物开发中减少对昂贵和耗时的实验方法的依赖.
- 建立一个框架,以便在制药研究中更广泛地采用生成AI.
主要方法:
- 开发了一个基于DeepSeek的LLM框架,结合了快速工程技术 (零射击,少数射击,思维链) 和自适应加权检索增强生成 (RAG).
- 使用扩散模型从扫描电子显微镜 (SEM) 图像中合成形态参数,绕过多仪器表征的错误.
- 这些合成的参数被集成到RAG数据库中,以在结构-性能关系中对LLM预测进行基准.
主要成果:
- 该LLM,特别是使用RAG的少数拍摄思维链,在预测和实验溶解配置文件之间显示出良好的一致性.
- 灵敏度分析量化了快速工程策略的可靠性和稳定性.
- 扩散生成的结构数据与LLM预测的整合成功地将宏观物理属性与微观结构特征联系起来,显示了与可接受错误指标 (RMSE和PCC) 密切的趋势.
结论:
- 基于DeepSeek的LLM框架显示了描述药物粉末溶解行为的巨大潜力.
- 随着RAG的短暂连锁思维,RAG成为最有效的迅速策略,用于溶解配置预测.
- 扩散模型和LLM的组合有效地将人工智能驱动的预测与物理和结构性药物特性相结合,为增强药物开发铺平了道路.
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