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

10.9K
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...
10.9K
Drug Nomenclature01:17

Drug Nomenclature

3.1K
During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that...
3.1K
Cognitive Enhancers: Cholinesterase Inhibitors and NMDA Receptor Antagonists01:30

Cognitive Enhancers: Cholinesterase Inhibitors and NMDA Receptor Antagonists

544
Cognitive enhancers, also known as "smart drugs," are substances used to enhance memory, mental alertness, and concentration. These can be natural or synthetic and improve cognition in conditions like Alzheimer's disease (AD) and other neurodegenerative diseases. Some common examples include caffeine, amphetamines, methylphenidate, modafinil, arecoline, donepezil, vortioxetine, and piracetam. These enhancers work on the principle of synaptic plasticity and altered circuit function.
544
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.7K
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...
1.7K
Principles of Drug Action01:24

Principles of Drug Action

8.0K
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...
8.0K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

10.0K
Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
10.0K

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

Unveiling the neurotoxic mechanism of amino-functionalized graphene quantum dots: Mitophagy-driven ferroptosis underlies progressive anxiety-like behaviors.

Journal of hazardous materials·2026
Same author

Microlipophagy-mediated lipid remodeling contributes to radioresistant phenotypes in small cell lung cancer.

Archives of biochemistry and biophysics·2026
Same author

Cu(OH)<sub>2</sub> nanopesticide triggered heart failure-like pathogenesis in mice by potentially targeting mmu-miRNA-590-3p and mmu-miRNA-338-5p in Wnt/β-catenin signaling.

Particle and fibre toxicology·2026
Same author

Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction.

Applications of medical artificial intelligence. AMAI (Workshop) (4th : 2024 : Taejon-si, Korea)·2026
Same author

Cu(OH)<sub>2</sub> nanopesticide induced adolescent social behavior deficits via long noncoding RNA-mediated synaptic network dysfunction.

Journal of hazardous materials·2026
Same author

Phytotoxicity of flufenoxuron in barley: Disrupted crosstalk between auxin signaling and carbon metabolism.

Ecotoxicology and environmental safety·2026

相关实验视频

Updated: Jan 13, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

10.2K

利迪亚:基于语言的智能药物发现代理

Reza Averly1, Frazier N Baker1, Ian A Watson2

  • 1Department of Computer Science and Engineering, The Ohio State University, USA.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|January 6, 2026
PubMed
概括

我们开发了LIDDiA,这是一种用于自主药物发现的AI代理. LIDDiA有效地导航复杂的药物发现过程,生成分子并识别癌症治疗的新药候选者.

更多相关视频

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B
11:44

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

Published on: January 19, 2022

2.9K
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

1.3K

相关实验视频

Last Updated: Jan 13, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

10.2K
Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B
11:44

Cellular Membrane Affinity Chromatography Columns to Identify Specialized Plant Metabolites Interacting with Immobilized Tropomyosin Kinase Receptor B

Published on: January 19, 2022

2.9K
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

1.3K

科学领域:

  • 化学领域的人工智能
  • 计算机化药物发现.
  • 药品化学 药品化学 是一个

背景情况:

  • 药物发现是一个漫长,昂贵和复杂的过程,严重依赖于人类的专业知识.
  • 现有的人工智能 (AI) 工具可以加速特定任务,但缺乏端到端导航功能.
  • 需要一个智能系统来自主指导药物发现管道.

研究的目的:

  • 介绍LIDDiA,一个自主的人工智能代理设计用于in silico药物发现.
  • 为了证明LIDDiA在整个药物发现过程中的导航能力.
  • 将 LIDDiA 呈现为一种低成本,可适应的解决方案,利用大型语言模型.

主要方法:

  • 开发LIDDiA,一个使用大型语言模型进行推理的自主代理.
  • 在30个临床相关目标中对LIDDiA进行了体评估.
  • 评估LIDDiA在化学空间中平衡勘探和开发的能力.

主要成果:

  • 在超过70%的测试目标中,LIDDiA成功生成了符合制药标准的分子.
  • 代理商在化学搜索空间内展示了智能勘探和开发.
  • 确定了一种针对AR/NR3C4的新型候选药物,该药物对前列腺癌和乳腺癌至关重要.

结论:

  • LIDDiA代表了自主药物发现的重大进展.
  • 人工智能代理提供了一种具有成本效益和适应性的方法来识别潜在的治疗方法.
  • LIDDiA成功地确定了AR/NR3C4的有希望的候选者,突出了其在瘤学药物开发中的潜力.