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

Drug Discovery: Overview01:26

Drug Discovery: Overview

8.0K
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...
8.0K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.0K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.0K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

734
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...
734
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

89
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
89
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.9K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

5.3K
Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
5.3K

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

Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space.

Scientific data·2026
Same author

Scalable emulation of protein equilibrium ensembles with generative deep learning.

Science (New York, N.Y.)·2025
Same author

The changing landscape of medicinal chemistry optimization.

Nature reviews. Drug discovery·2025
Same author

Balancing Molecular Size, Activity, Permeability, and Other Properties: Drug Candidates in the Context of Their Chemical Structure Optimization.

Journal of chemical information and modeling·2024
Same author

Discovery of Potent, Orally Bioavailable, Tricyclic NLRP3 Inhibitors.

Journal of medicinal chemistry·2024
Same author

Prediction of Small-Molecule Developability Using Large-Scale <i>In Silico</i> ADMET Models.

Journal of medicinal chemistry·2023

相关实验视频

Updated: Jul 12, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

通过偏好机器学习提取药物化学直觉.

Oh-Hyeon Choung1, Riccardo Vianello1, Marwin Segler2

  • 1Novartis Institutes for Biomedical Research, 4002, Basel, Switzerland.

Nature communications
|November 1, 2023
PubMed
概括

人工智能学习到等级模型被训练在药物化学家反上,以加快药物发现的领先优化. 这些人工智能工具有助于化合物优先级和新药设计,大大缩短了开发时间.

更多相关视频

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.6K
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

2.0K

相关实验视频

Last Updated: Jul 12, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.6K
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

2.0K

科学领域:

  • 药用化学 医学化学
  • 人工智能的人工智能
  • 药物发现 药物发现 药物发现

背景情况:

  • 药物发现中的优化是复杂的,需要药物化学家的广泛专业知识和时间.
  • 对分子性质配置文件的合作决策是一个漫长的,专业知识驱动的过程.

研究的目的:

  • 使用AI复制协作领先优化流程.
  • 开发人工智能模型,从专家药物化学家反中学习.
  • 通过智能自动化加速药物发现时间表.

主要方法:

  • 应用人工智能学习到等级的技术.
  • 在几个月内利用了诺华公司35名药品化学家的反.
  • 在注释的响应数据上训练模型.

主要成果:

  • 开发了模仿专家决策在优化中的AI代理.
  • 证明了在复合优先排序中学习代理的实用性.
  • 展示了动机合理化和偏见的新药设计中的应用.

结论:

  • 人工智能驱动的方法可以有效地复制和加速专家驱动的优化.
  • 开发的模型和代码是开源的,这有助于更广泛的采用.
  • 这项工作为药物发现的耗时方面提供了一个可扩展的解决方案.