大型模型时代:骨质疏松症药物发现中的深度学习
Junlin Xu1,2, Xiaobo Wen1,2,3, Li Sun1,2,4
1The Affiliated Hospital of Qingdao University, Qingdao Medical College, Qingdao University, Qingdao 266071, China.
Journal of chemical information and modeling
|February 26, 2025
概括
深度学习,特别是大型模型,通过克服传统挑战,加速骨质疏松症药物发现. 本综述探讨了它们在药物开发阶段的应用,为未来的研究提供了见解.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 骨质疏松症是一种骨组织退化,导致骨折和降低生活质量.
- 对骨质疏松症的药物发现面临着长期开发周期和高成本等挑战.
- 深度学习 (DL) 为药物发现提供了强大的数据处理.
研究的目的:
- 在药物发现中审查传统和大型DL模型.
- 总结DL在骨质疏松症药物发现的所有阶段的应用.
- 分析这个领域的大型模型的前景和局限性.
主要方法:
- 对药物发现中的深度学习应用进行系统审查.
- 分析传统和大型DL模型.
- 讨论模型的优点和局限性.
主要成果:
- 深度学习模型越来越多地应用于药物发现阶段.
- 大型模型为了解疾病机制和药物开发提供了新的机会.
- 详细介绍了骨质疏松症药物发现的具体应用和前景.
结论:
- 深度学习,特别是大型模型,显示了有效的骨质疏松症药物发现的重大前景.
- 了解大型模型的优点和局限性对于未来的研究至关重要.
- 本综述提供了一个全面的概述,以指导未来的药物发现工作.
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