生物物理中的机器学习:从生物分子预测到设计
Jonathan Martin1, Marcos Lequerica Mateos2, José N Onuchic3,4,5,6
1Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080.
概括
结合物理建模和机器学习,为复杂的生物物理问题提供了强大的方法. 这种融合增强了生物分子结构预测,动态和蛋白质设计.
科学领域:
- 生物物理 生物物理
- 计算神经科学是一种神经科学.
- 生物分子工程 生物分子工程
背景情况:
- 机器学习 (ML) 在生物物理学中越来越多地使用,但通常与理论建模分开.
- 早期的计算神经网络,如霍普菲尔德网络,起源于神经元过程的物理建模.
研究的目的:
- 倡导和展示物理建模和机器学习在生物物理中的协同集成.
- 探索各种ML方法与物理模型之间的联系,特别是通过能量表示.
主要方法:
- 讨论物理建模与早期神经网络之间的历史联系.
- 通过共享的能量表示框架分析现代的ML方法 (Potts模型,博尔兹曼机器,变压器).
- 审查了最近在生物分子研究中集成物理建模和ML的成功应用.
主要成果:
- 建立了各种ML技术和物理建模原则之间的联系.
- 突出了蛋白质结构预测,分子动力学和进化建模方面的成功.
- 证明了ML对蛋白质工程和设计的革命性影响,包括生成新型蛋白质序列.
结论:
- 物理建模和机器学习的整合为应对复杂的生物物理挑战提供了更成功的框架.
- 这种综合方法在理解和操纵生物分子系统方面取得了重大进展.
- 未来的工作包括使用可学习的物理模型来生成具有所需结构的独特合成蛋白序列.
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