在RNA-小分子结合部位预测方面的计算进步.
Lang Yang1, Zou Yan1, Yanhui Liu1
1School of Physics, Guizhou University, Guiyang, Guizhou 550000, China.
Progress in biophysics and molecular biology
|February 7, 2026
概括
计算方法正在进步,以预测RNA-连接体结合部位,这对于开发新的RNA向药物至关重要. 本综述涵盖了从机器学习到大型语言模型的不断发展的策略,以克服预测挑战.
科学领域:
- 生物化学和分子生物学
- 计算生物学和化学信息学
- 药物发现和药物化学
背景情况:
- RNA-小分子相互作用对细胞过程至关重要,并代表着有前途的治疗点.
- 由于RNA的灵活性,动态结合点和缺乏结构数据,发现RNA结合分子是很困难的.
- 计算方法对于预测这些相互作用至关重要.
研究的目的:
- 审查计算策略的演变,以预测RNA-连接体结合部位.
- 突出多式联运特征的整合和现场当前挑战.
- 讨论准确和可概括的RNA向药物发现的未来方向.
主要方法:
- 从统计模型向机器学习 (ML) 和深度学习 (DL) 框架的演变.
- 序列,结构,能量和拓数据的整合.
- 大型语言模型 (LLM) 的应用用于基于序列的模式识别和多式模式建模.
主要成果:
- 现在的ML/DL模型包含了各种数据类型,以提高预测准确度.
- LLM 增强了对长距离序列依赖性和上下文信息的捕获.
- 结合序列和结构信息的多模式方法显示出有希望.
结论:
- 计算策略已经显著提升了RNA-连接体结合部位的预测.
- 整合包括LLM在内的多种数据模式是克服现有挑战的关键.
- 未来的工作应该专注于准确性,可概括性和可解释性,以加速药物发现.
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