Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Hot-Melt Extrusion of Bupropion with Three Ethylcellulose Grades for Pellet Feedstock Preparation and Screw-Based 3D Printing of Sustained-Release Tablets.

AAPS PharmSciTech·2026
Same author

Recent advances in intraoral, buccal and sublingual drug delivery systems and technologies.

Journal of controlled release : official journal of the Controlled Release Society·2026
Same author

High-throughput identification of bacterial β-glucuronidase inhibitors using machine learning.

Gut microbes·2026
Same author

Biorelevant assessment of commercial probiotics using porcine gastrointestinal fluids.

Journal of applied microbiology·2026
Same author

A water-based probiotic supplement shows antipathogenic activity against Clostridium perfringens, Klebsiella pneumoniae, and Listeria monocytogenes.

International journal of pharmaceutics·2026
Same author

A multi-strain probiotic modulates gut microbiome composition, intestinal barrier integrity and inflammation in a multi-compartmental in vitro gut model of decompensated advanced chronic liver disease.

International journal of pharmaceutics·2026

相关实验视频

Updated: Feb 26, 2026

3D Printing of Preclinical X-ray Computed Tomographic Data Sets
11:06

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Published on: March 22, 2013

41.1K

药品3D打印中的积极学习:一个多数据集比较.

Moe Elbadawi1, Noorul Fathima Abdul Kafoor2, Hanxiang Li3

  • 1School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End Road, London,, E1 4DQ, UK. m.elbadawi@qmul.ac.uk.

Drug delivery and translational research
|February 24, 2026
PubMed
概括

积极学习 (AL) 通过使用小数据集实现机器学习 (ML) 来加速3D打印药物的开发. 这种方法在预测制药3D打印成功方面实现了100%的准确性.

关键词:
积极的机器学习.增材制造 增材制造是一种增材制造.人工智能的人工智能是人工智能.计算建模计算建模药物开发 药物开发在状的中.可持续发展 可持续性 可持续性

更多相关视频

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing
11:36

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing

Published on: February 9, 2022

3.3K
Multimodal 3D Printing of Phantoms to Simulate Biological Tissue
05:11

Multimodal 3D Printing of Phantoms to Simulate Biological Tissue

Published on: January 11, 2020

8.1K

相关实验视频

Last Updated: Feb 26, 2026

3D Printing of Preclinical X-ray Computed Tomographic Data Sets
11:06

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Published on: March 22, 2013

41.1K
Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing
11:36

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing

Published on: February 9, 2022

3.3K
Multimodal 3D Printing of Phantoms to Simulate Biological Tissue
05:11

Multimodal 3D Printing of Phantoms to Simulate Biological Tissue

Published on: January 11, 2020

8.1K

科学领域:

  • 制药制造业 制药制造业 制药制造业
  • 计算化学计算化学
  • 材料科学 材料科学 材料科学

背景情况:

  • 机器学习 (ML) 为推进3D打印药物提供了巨大的潜力.
  • 药品中3D打印技术的发展往往受到ML模型培训需要广泛数据集的限制.
  • 新兴的制药制造技术,如3D打印,需要创新的数据利用方法.

研究的目的:

  • 研究主动学习 (AL) 的有效性,这是一种机器学习策略,用于预测3D打印制药配方的可打印性.
  • 用有限的数据集评估AL的性能,解决制药3D打印中的一个关键挑战.
  • 在3D打印药物的背景下,将AL的预测精度与传统的ML方法进行比较.

主要方法:

  • 积极学习 (AL) 用于预测三种不同的3D打印技术中的配方可打印性:沉积建模 (FDM),聚合和选择性激光烧结 (SLS).
  • 这项研究使用了三组数据集,不同数量的配方 (1437个FDM,650聚合,297个SLS).
  • 根据预测准确度评估模型性能,随着训练数据集的大小的增加.

主要成果:

  • 积极学习 (AL) 实现了60%的预测准确度,从最少33种配方开始.
  • 增加培训数据大小进一步提高了AL模型的预测性能.
  • 该研究使用AL记录了100%的预测准确性,这是迄今为止在制药3D打印应用中报告的最高水平.
  • 与这些数据集的传统机器学习方法相比,AL表现优越.

结论:

  • 积极学习 (AL) 是加速3D打印药物的开发的可行和有效策略,特别是在处理有限数据时.
  • 这项研究验证了机器学习 (ML) 用小数据集建模的潜力,扩大了其在制药研发中的适用性.
  • 这些发现表明,AL可以显著提高3D打印制药配方的效率和成功率.