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

Machine learning for skin permeability prediction: random forest and XG boost regression.

Journal of drug targeting·2023
Same author

Response to: Regarding the Article: Coronavirus Disease (COVID-19): Current Status and Prospects for Drug and Vaccine Development.

Archives of medical research·2021
Same author

COVID-19 Vaccines: New Developments and the Road Ahead.

Archives of medical research·2020
Same author

Coronavirus Disease (COVID-19): Current Status and Prospects for Drug and Vaccine Development.

Archives of medical research·2020
Same author

Polyplexes for gene and nucleic acid delivery: Progress and bottlenecks.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences·2020
Same author

Percutaneous Delivery of Antihypertensive Agents: Advances and Challenges.

AAPS PharmSciTech·2020

相关实验视频

Updated: Jul 5, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
10:33

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

Published on: February 23, 2018

25.3K

人工智能用于皮肤透性预测:深度学习

Kevin Ita1, Sahba Roshanaei1

  • 1College of Pharmacy, Touro University, Vallejo, CA, USA.

Journal of drug targeting
|January 23, 2024
PubMed
概括

包括卷积神经网络在内的深度学习模型可以准确预测外来生物的皮肤透性. 这为药物输送研究提供了比传统实验室测量更快的替代方案.

科学领域:

  • 药理动力学和药物输送方法
  • 计算化学计算化学
  • 人工智能在医学中的应用

背景情况:

  • 测量异生菌的皮肤透性 (Kp) 是劳动密集型的.
  • 定量结构-透性关系 (QSPR) 模型通常依赖于实验数据的统计分析.
  • 深度学习,使用深度神经网络 (DNN) 的机器学习的一个子集,正在获得计算药物递送的引力.

研究的目的:

  • 探索深度学习模型对预测皮肤透系数的有用性.
  • 开发替代,高效的方法来评估通过皮肤的异生菌运输.
  • 在此背景下,研究卷积神经网络 (CNN),前神经网络 (FNN) 和循环神经网络 (RNN) 的预测性能.

主要方法:

  • 利用公开可用的数据集,包括145种化学品和药品的476个记录.
  • 应用CNN,FNN和RNN架构来预测皮肤透系数 (log kp).
  • 在Anaconda和Jupyterlab环境中使用Python进行计算,使用Keras和TensorFlow模块.

主要成果:

  • 通过CNN,FNN和RNN模型成功预测了log kp值.
  • 证明了深度学习网络模拟皮肤透性的能力.
  • 该研究验证了所选择的深度学习架构的预测能力.
关键词:
皮肤的透性深度学习是一种深度学习.描述者描述者是指描述者.神经网络的神经网络的神经网络通过皮肤通过皮肤.

更多相关视频

Generation of a Simplified Three-Dimensional Skin-on-a-chip Model in a Micromachined Microfluidic Platform
06:30

Generation of a Simplified Three-Dimensional Skin-on-a-chip Model in a Micromachined Microfluidic Platform

Published on: May 17, 2021

4.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

相关实验视频

Last Updated: Jul 5, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
10:33

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

Published on: February 23, 2018

25.3K
Generation of a Simplified Three-Dimensional Skin-on-a-chip Model in a Micromachined Microfluidic Platform
06:30

Generation of a Simplified Three-Dimensional Skin-on-a-chip Model in a Micromachined Microfluidic Platform

Published on: May 17, 2021

4.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

结论:

  • 深度学习网络是数字查和预测外来生物皮肤透性的有效工具.
  • 这种方法可以显著简化药物开发过程.
  • 这些发现支持人工智能在预测药物运输特性方面的更广泛应用.