学习物理交互来构建生物的大型语言模型
Joseph D Clark1, Tanner J Dean2, Diwakar Shukla3,4,5,6
1School of Molecular and Cellular Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Communications chemistry
|January 7, 2026
概括
药物设计中的大型语言模型可以通过合并分子表示来改进. 结合模型可以提高分子相互作用的预测,促进药物发现和开发.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 医学中的人工智能
背景情况:
- 深度学习模型,特别是大型语言模型 (LLM),是现代药物设计的组成部分,通过生物化学序列的特征向量来帮助虚拟选.
- 目前的LLM缺乏完全捕捉影响结合亲和力和特异性的关键分子相互作用的能力.
研究的目的:
- 通过探索合并多样化分子表示的方法来解决现有模型的局限性.
- 建议开发能够联合编码多种生物数据类型的生物化学基础模型,以提高相互作用预测.
主要方法:
- 现有方法的概述,用于组合来自不同生物模式的分子表示.
- 生物化学语言模型的"构成"策略的开发和应用,合并内部层表示.
- 对解释和民主化生物应用LLM的最新进展进行分析.
主要成果:
- 拟议的"构成"生物化学语言模型的方法显示性能与分子相互作用预测的标准方法相当或超过.
- 组合型号以显著减少的功能集实现了这种性能.
- 该研究强调了合并内部表示的潜力,以提高相互作用预测中的概括性.
结论:
- 来自不同的生物模式的融合表示对于开发更有效的分子相互作用预测模型至关重要.
- 未来的生物化学基础模型应该被设计为共同编码多种分子数据,以全面理解.
- 这种方法为更准确和更普遍的药物发现工具提供了一条道路,有潜力预测分子相互作用的进化变化.
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