在临床前抗体药物发现和开发中的深度学习
Yuwei Zhou1, Ziru Huang1, Wenzhen Li1
1School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
Methods (San Diego, Calif.)
|July 16, 2023
概括
人工智能,特别是深度学习,加速了临床前抗体药物开发. 本综述详细介绍了人工智能方法,应用和未来的方向,以实现更高效的生物治疗发现.
科学领域:
- 生物治疗学 生物治疗学
- 药物发现 药物发现 药物发现
- 人工智能的人工智能
背景情况:
- 抗体药物是重要的生物治疗药物,为患者提供各种疾病的显著益处.
- 传统的抗体药物开发是漫长,昂贵和高风险的.
- 人工智能 (AI) 和深度学习 (DL) 越来越多地被采用来克服这些挑战.
研究的目的:
- 系统地审查用于临床前抗体药物发现中的抗体编码,DL架构和模型.
- 批判性地讨论DL在这个领域的挑战,机会,问题,解决方案,应用和未来方向.
主要方法:
- 文献综述和AI和DL方法的系统总结.
- 在临床前发展阶段对抗体编码和DL模型应用的分析.
- 批判性地讨论当前的研究和未来的趋势.
主要成果:
- 在药物发现中使用的各种抗体编码和DL架构的识别.
- 跨临床前抗体开发阶段的DL应用概述 (图书馆生成到优化).
- 讨论DL实施的关键挑战和潜在解决方案.
结论:
- 深度学习显著提高了临床前抗体药物开发的效率和成功率.
- 对DL应用的进一步研究有望彻底改变生物治疗的发现.
- 应对当前的挑战将释放AI在抗体工程中的全部潜力.
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