核酸分子工程的机器学习驱动的分子工程
1State Key Laboratory of Synergistic Chem-Bio Synthesis, School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, New Cornerstone Science Laboratory, Zhang Jiang Institute for Advanced Study, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240, China. limingqiang@sjtu.edu.cn.
Chemical Society reviews
|March 12, 2026
概括
机器学习 (ML) 通过实现基因疗法和生物传感的数据驱动设计,彻底改变了核酸工程. 这种方法克服了预测序列结构功能方面的挑战,为先进的生物医学创新铺平了道路.
科学领域:
- 生物医学工程 生物医学工程
- 分子生物学分子生物学
- 计算生物学是一种计算生物学.
背景情况:
- 分子工程推动了基因疗法,疾病诊断和生物传感方面的进步.
- 核酸工程在设计空间,结构功能预测和优化方面面临着挑战.
- 当前的方法通常是经验驱动的,导致漫长的验证周期.
研究的目的:
- 系统地审查核酸分子工程机器学习 (ML) 应用的最新进展.
- 探索ML在构建序列结构功能关系的预测模型方面的潜力.
- 识别ML驱动的核酸工程中的核心挑战和潜在解决方案.
主要方法:
- 对核酸工程中ML应用的系统文献综述.
- 分析ML在结构构建,性能调制和应用扩展中的作用.
- 讨论包括数据质量,模型解释性和实验验证等挑战.
主要成果:
- 机器学习能够实现数据驱动的方法,从经验方法转向预测建模.
- ML的应用包括核酸结构构造,性能调制和各种应用.
- 关键的挑战包括数据质量,模型可解释性和高效的实验验证.
结论:
- 机器学习促进了核酸工程向动态行为模拟和复杂系统设计的范式转变.
- 未来的方向包括混合ML-量子模型和对非正规核酸的应用.
- 这些进展有望在生物医学,环境监测和信息技术方面带来变革性的创新.
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