Jove
Visualize
联系我们

相关概念视频

Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

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

Deep learning-enabled discovery of antibiotics effective against <i>Neisseria gonorrhoeae</i>.

Science translational medicine·2026
Same author

Focused ultrasound in veterinary medicine.

Veterinary journal (London, England : 1997)·2026
Same author

Mitoception Via the Metabokine GDF15 and Human Health.

Biopsychosocial science and medicine·2026
Same author

Mapping the GDF15 arm of the integrated stress response in human cells and tissues.

Communications biology·2026
Same author

Space-time acoustofluidic tweezers for dynamic and selective manipulation of microparticles.

Science advances·2026
Same author

Optogenetic Control of the Integrated Stress Response Limits Glioblastoma Invasion.

Cell biochemistry and function·2026
Same journal

iMUT-seq mapping of DSB-induced mutations with high sensitivity at single-nucleotide resolution.

Nature protocols·2026
Same journal

An assay to quantify sexual commitment and stage conversion in the human malaria parasite Plasmodium falciparum.

Nature protocols·2026
Same journal

Author Correction: Direct inoculation of bioreactor-controlled stirred suspension culture with cryopreserved human pluripotent stem cells.

Nature protocols·2026
Same journal

High-throughput measurements of protein domain functions using magnetic separation.

Nature protocols·2026
Same journal

Inducing physiological polarity and performing gene editing using CRISPR-Cas9 in human trophoblast organoids.

Nature protocols·2026
Same journal

Photocatalytic low-temperature defluorination of PTFE.

Nature protocols·2026
查看所有相关文章
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

相关实验视频

Updated: Jun 17, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K

一个可解释的深度学习平台,用于分子发现.

Felix Wong1,2,3, Satotaka Omori1,3, Alicia Li3

  • 1Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Nature protocols
|December 9, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个可解释的深度学习平台,用于发现新型化学化合物. 它识别了活跃的结构类,增强了药物发现和化学太空探索,而不需要编码专业知识.

更多相关视频

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

673
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

954

相关实验视频

Last Updated: Jun 17, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

673
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

954

科学领域:

  • 计算化学是一种计算化学.
  • 人工智能在药物发现中的作用
  • 可解释的人工智能 (XAI)

背景情况:

  • 深度学习模型加速新型化合物发现,但往往充当黑子,限制化学洞察力.
  • 可解释的深度学习 (XDL) 旨在为AI预测提供可理解的推理.
  • 识别活性结构类,而不仅仅是单个化合物,可以显著提高药物发现效率.

研究的目的:

  • 提出一个可解释的深度学习平台,用于挖掘广的化学空间,并确定与所需活动相关的关键子结构.
  • 为了使分子的活性结构类的发现,最初专注于抗生素.
  • 为数据生成,模型实现和可解释性评估提供一个用户友好的协议.

主要方法:

  • 利用Chemprop,一个使用图形神经网络 (GNN) 进行分子性质预测的软件包.
  • 开发了一种用于实验数据生成,模型培训和可解释性评估的协议.
  • 专注于确定具有所需活性的抗生素的结构类别.

主要成果:

  • 展示了一个可解释的深度学习平台,能够挖掘大型化学空间并精确定位活性化学子结构.
  • 成功地应用了这个平台来发现抗生素的结构类.
  • 该协议不需要编码能力或专门的硬件,可在1-2周内执行.

结论:

  • 开发的平台有效地整合了可解释的深度学习,用于增强分子发现.
  • 它有助于识别活跃的结构类,指导假设生成和优化化学空间探索.
  • 该平台的广泛适用性扩展到发现各种小分子 (抗癌,抗病毒,老化) 和具有特定性质的无机分子.