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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.0K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.0K
Drug Discovery: Overview01:26

Drug Discovery: Overview

8.7K
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...
8.7K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

110
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
110
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
126
Principles of Drug Action01:24

Principles of Drug Action

6.6K
Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
Drugs can be agonists or antagonists. Like the endogenous ligands, agonists always bind and activate the target to produce a cellular response. Agonist binding induces a conformational change which in turn...
6.6K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.1K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.1K

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

Structural insights into a conserved mechanism of choline translocation through CHT.

Science advances·2026
Same author

<i>In Silico</i> Optimization of a Bifunctional Lipase-Polyethylene Terephthalate (PET) Hydrolase for Enhanced PET and Lipid Hydrolysis.

Journal of chemical information and modeling·2026
Same author

Targeted Preoperative Nutritional Support within an Enhanced Recovery After Surgery Program for Malnourished Colorectal Cancer Patients: Postoperative Outcomes Comparable to Well-Nourished Patients in a Prospective Single-Center Cohort.

Annals of surgical oncology·2026
Same author

Molecular Insights into the Activation of a Fungal Copper Radical Oxidase by Peroxidases.

Chem & bio engineering·2026
Same author

A la carte bioprospecting of substrate-selective laccases via high-throughput computational enzyme-substrate interaction profiling.

Protein science : a publication of the Protein Society·2026
Same author

Unraveling the impact of cyclic peptide primary structure on rotaxane formation through umbrella sampling molecular dynamics simulations.

Physical chemistry chemical physics : PCCP·2026
Same journal

Assessing crystallisation behaviour in molecular crystals through particle rugosities.

Communications chemistry·2026
Same journal

Machine-learning-assisted continuous flow synthesis of clonidine.

Communications chemistry·2026
Same journal

A combined computational and experimental approach to revisit the Butlerov reaction.

Communications chemistry·2026
Same journal

Structure and mechanism of inhibition of lysine demethylase 2A (KDM2A) by compound 183c.

Communications chemistry·2026
Same journal

Recyclable glass fiber-reinforced epoxy copper clad laminates for printed circuit board.

Communications chemistry·2026
Same journal

Photolytic disruption of Alzheimer's amyloid Aβ<sub>42</sub>-fibrils by sialic-acid decorated glycodendrimers.

Communications chemistry·2026
查看所有相关文章

相关实验视频

Updated: Sep 12, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

527

通过将生成AI与基于物理的积极学习框架合并,优化药物设计.

Isaac Filella-Merce1, Alexis Molina2, Lucía Díaz2

  • 1Barcelona Supercomputing Center (BSC), Barcelona, Spain.

Communications chemistry
|August 8, 2025
PubMed
概括
此摘要是机器生成的。

用积极学习来增强药物发现的生成模型. 这个工作流成功地设计了具有高预测亲和力和合成可访问性的新型分子,用于CDK2和KRAS目标.

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

578
Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
10:24

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

6.5K

相关实验视频

Last Updated: Sep 12, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

527
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

578
Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
10:24

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

6.5K

科学领域:

  • 计算化学和化学信息学
  • 人工智能在药物发现中的作用
  • 药物化学和分子设计.

背景情况:

  • 机器学习,特别是生成模型 (GMs),显示出在药物发现中设计具有所需性质的分子的前景.
  • 现有的转基因在实现目标参与,合成可访问性和强大的泛化方面面临挑战.
  • 解决这些局限性对于推进人工智能驱动药物设计至关重要.

研究的目的:

  • 为药物发现开发和验证一个改进的生成模型工作流程.
  • 通过将积极学习与化学信息学和分子建模预测器相结合,增强分子设计.
  • 为了产生新的,类似药物的分子,具有高预测亲和力和针对特定生物点的合成可访问性.

主要方法:

  • 开发了一个生成模型工作流程,将一个变化自编码器与两个嵌套的积极学习周期结合起来.
  • 使用化学信息学和分子建模工具,代精细化分子预测.
  • 应用工作流程来设计向循环林依赖激酶2 (CDK2) 和KRAS蛋白的分子.

主要成果:

  • 成功生成多样化,具有高预测亲和力和合成可访问性的药物样分子,适用于CDK2和KRAS.
  • 为每个目标发现了与已知的抑制剂不同的新型分子支架.
  • 合成了9个CDK2分子,其中8个显示了体外活性 (其中一个具有纳米分子强度);确定了4个潜在的KRAS抑制剂.

结论:

  • 开发的生成模型工作流程有效地探索了针对特定目标量身定制的新化学空间.
  • 该工作流显示了加速发现强效和可合成的候选药物的巨大潜力.
  • 这种方法为人工智能驱动的药物发现开辟了新的途径,克服了传统生成模型的局限性.