人工智能和机器学习引导药物输送的优化
Helena Ros1, Natasha Chan1, Michael T Cook1
1UCL School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, United Kingdom.
Advanced drug delivery reviews
|January 24, 2026
概括
人工智能 (AI) 和机器学习 (ML) 为优化药物输送系统提供了先进的解决方案. 这些数据驱动的方法加速开发,减少实验,并发现新的配方,以获得更好的治疗结果.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 生物技术是生物技术.
背景情况:
- 药物输送系统的优化面临着由于复杂性,高维度和多目标要求的挑战.
- 传统方法与现代药物输送问题作斗争,需要先进的方法.
研究的目的:
- 审查人工智能 (AI) 和机器学习 (ML) 对药物配方科学的变革性影响.
- 提供ML引导优化工作流程及其在药物输送中的应用的概述.
主要方法:
- 探索AI/ML技术,包括代理建模,贝叶斯优化,主动学习和多目标优化.
- 对各种药物输送方式的应用进行批判性审查.
主要成果:
- 人工智能和机器学习使得药物输送的数据驱动,自适应和高效的优化策略成为可能.
- 这些方法加速了配方开发,减少了实验负担,并确定了新的设计空间.
结论:
- 人工智能和机器学习正在彻底改变合理的药物设计和优化先进的药物输送系统.
- 未来的方向包括将人工智能集成到制药研发中,从而实现自动驾驶实验室.
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