对抗体生成模型的最新进展进行了全面的概述
Fanxu Meng1, Na Zhou2,3, Guangchun Hu4
1College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, China.
Computational and structural biotechnology journal
|July 19, 2024
概括
本综述将34个抗体生成模型分为序列,结构和混合型. 它分析了它们在抗体开发中的表现,帮助研究人员选择最佳的深度学习方法.
科学领域:
- 生物制药产品 生物制药产品
- 计算生物学 计算生物学
- 人工智能在药物发现中的作用
背景情况:
- 治疗性抗体是重要的生物制药.
- 深度学习和抗体数据的进步促进了抗体生成模型的进步.
- 这些模型越来越多地集成到抗体开发工作流程中.
研究的目的:
- 为最近的抗体生成模型提供全面的概述.
- 根据它们的基本原则和算法对这些模型进行分类.
- 引导研究人员选择适当的抗体生成策略.
主要方法:
- 34个代表性抗体生成模型的系统收集和分析.
- 将模型分为三个主要类别:序列生成,结构生成和混合模型.
- 在抗体序列预测,结构优化和亲和力增强方面评估模型性能.
主要成果:
- 抗体生成模型可以广泛分为序列,结构和混合类型.
- 这些模型对预测抗体序列,优化结构和增强亲和力作出了重大贡献.
- 该研究提供了对各种生成建模技术的能力和应用的见解.
结论:
- 抗体生成模型是一个快速发展的领域,对生物制药开发产生重大影响.
- 了解不同类别及其性能对于有效应用至关重要.
- 本综述作为一个宝贵的资源,用于导航抗体生成模型的景观.
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