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使用主动学习和自动化实验进行纳米医学优化的数据驱动工作流

Zeqing Bao¹, Frantz Le Devedec¹, Steven Huynh²

¹Acceleration Consortium, University of Toronto, Toronto, Ontario M5S 3H6, Canada.

Molecular pharmaceutics

|October 25, 2025

概括

这项研究引入了一种人工智能驱动的工作流程,以加速纳米医学开发. 它有效地识别了最佳的纳米配方,提高了溶解度和稳定性,克服了传统的局限性.

科学领域:

制药科学制药科学
材料科学材料科学材料科学
计算化学计算化学

背景情况:

纳米药物为疏水药物提供了增强的溶解性.
目前的纳米医药开发效率低下,阻碍了配方优化.
开发最佳的纳米配方需要系统的选和微调.

研究的目的:

开发一个集成积极学习和实验自动化的数据驱动工作流,以快速识别最佳纳米配方.
克服当前纳米医药开发方法的局限性.
为了加速发现高性能纳米配方,用于溶解不良的药物.

主要方法:

一个积极学习的机器人系统被用来导航一个巨大的配方设计空间 (17亿个可能性).
一种实验设计方法改进了对选定的配方的搜索空间.
进行了纳米配方的手动制备,净化和表征.

主要成果:

一组高性能纳米配方在几周内被确定.
识别的纳米配方证明了提高溶解度,小且均的颗粒大小和存储稳定性.
工作流显著加快了最佳纳米配方候选人的识别.

结论:

将人工智能驱动的设计与自动化相结合,加速了纳米医学的发展.
这种方法使难溶性药物的有效配方开发成为可能.
工作流程为更有效和系统的纳米医学发现奠定了基础.

关键词:

贝叶斯优化是贝叶斯的优化.积极学习是积极学习.自动化实验的实验.纳米医药是一种纳米医药.自动驾驶实验室自动驾驶实验室

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