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

Protein-protein Interfaces02:04

Protein-protein Interfaces

14.9K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
14.9K
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

53
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
53
Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase01:11

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase

54
Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
54
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K

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

GATSBI: Improving context-aware protein embeddings through biologically motivated data splits.

bioRxiv : the preprint server for biology·2026
Same author

Publisher Correction: CRISPR-GPT for agentic automation of gene-editing experiments.

Nature biomedical engineering·2025
Same author

SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology.

bioRxiv : the preprint server for biology·2025
Same author

Empirical Drug Dosage Validates Pharmacogenomic Associations in All of Us.

Clinical and translational science·2025
Same author

Reframing Justice in Healthcare AI: An Ubuntu-Based Approach for Africa.

Developing world bioethics·2025
Same author

Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research.

Nature machine intelligence·2025
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
查看所有相关文章

相关实验视频

Updated: Feb 28, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.8K

使用PIGLET进行药物向相互作用预测.

Kristy A Carpenter, Russ B Altman

    bioRxiv : the preprint server for biology
    |February 27, 2026
    PubMed
    概括
    此摘要是机器生成的。

    一种新的图形转换器方法,PIGLET,通过使用知识图来改善药物向相互作用的预测. 这种方法在严格的基于药物的分割上显示出卓越的性能,推进了计算药物发现.

    更多相关视频

    Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
    03:08

    Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

    Published on: October 3, 2025

    1.0K
    Use of a Piglet Model for the Study of Anesthetic-induced Developmental Neurotoxicity AIDN: A Translational Neuroscience Approach
    06:38

    Use of a Piglet Model for the Study of Anesthetic-induced Developmental Neurotoxicity AIDN: A Translational Neuroscience Approach

    Published on: June 11, 2017

    11.7K

    相关实验视频

    Last Updated: Feb 28, 2026

    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
    10:21

    Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

    Published on: February 23, 2024

    3.8K
    Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
    03:08

    Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

    Published on: October 3, 2025

    1.0K
    Use of a Piglet Model for the Study of Anesthetic-induced Developmental Neurotoxicity AIDN: A Translational Neuroscience Approach
    06:38

    Use of a Piglet Model for the Study of Anesthetic-induced Developmental Neurotoxicity AIDN: A Translational Neuroscience Approach

    Published on: June 11, 2017

    11.7K

    科学领域:

    • 计算生物学是一种计算生物学.
    • 药物发现 药物发现
    • 生物信息学是一种生物信息学.

    背景情况:

    • 药物向相互作用 (DTI) 的预测对于计算药物开发至关重要.
    • 目前用于DTI预测的深度学习模型虽然具有高性能,但实际影响有限.
    • 现有的方法通常依赖于简化的药物和目标表示.

    研究的目的:

    • 为DTI预测引入一种新的图形变压器方法.
    • 为了提高预测准确性,利用一个全面的蛋白质组范围的知识图.
    • 解决现有的DTI预测模型在加速药物发现方面的局限性.

    主要方法:

    • 开发了PIGLET,一个用于DTI预测的图形变压器模型.
    • 使用知识图,结合结合口袋相似性,蛋白质-蛋白质相互作用和药物相似性.
    • 基准PIGLET与人类数据集中的现有模型相比,使用随机和基于药物的分割.

    主要成果:

    • 与现有的模型相比,PIGLET在严格的基于药物的分割上表现出更好的表现.
    • 所有模型在传统的随机分割上都表现相似.
    • 该研究通过现实世界药物发现案例研究强调了PIGLET的实用性.

    结论:

    • PIGLET为DTI预测提供了一种更强大,更准确的方法.
    • 基于知识图的方法提高了对现实世界的应用预测的可靠性.
    • 这一进步有可能显著加速计算药物发现工作.