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相关概念视频

Drug Discovery: Overview01:26

Drug Discovery: Overview

8.8K
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...
8.8K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.1K
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...
1.1K
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

7.5K
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...
7.5K
Drug Clearance: Overview01:06

Drug Clearance: Overview

168
Drug elimination refers to drug removal from the body, either through urine or bile, by the kidneys or liver, respectively. A pharmacokinetic parameter, drug clearance, measures the efficiency of drug removal from the bloodstream within a specific time frame. It is calculated as the rate at which a drug is eliminated from plasma divided by the drug's concentration in plasma.
Drug clearance is not limited to renal excretion but encompasses all organs involved in drug elimination, including...
168
Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

2.4K
When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
2.4K
Drug Delivery: Overview01:16

Drug Delivery: Overview

429
The selection of a drug's delivery route depends upon its physicochemical properties, including lipid or water solubility and ionization, as well as the therapeutic requirement, such as immediate or sustained effect. These routes can be divided into three primary categories: enteral, parenteral, and topical.
Enteral delivery involves administering drugs directly through swallowing, sublingual placement, or buccal application. Orally administered drugs predominantly navigate the...
429

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相关实验视频

Updated: Sep 18, 2025

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

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人工智能驱动的药物发现:全面审查

Fábio J N Ferreira1, Agnaldo S Carneiro1

  • 1Universidade Federal do Pará, R. Augusto Corrêa, 01 - Guamá, Belém, Pará 66075-110, Brazil.

ACS omega
|June 23, 2025
PubMed
概括

人工智能 (AI) 和机器学习 (ML) 正通过提高效率和成功率来彻底改变药物发现. 本综述详细介绍了AI/ML的应用,从目标识别到临床开发,强调了创造更好的药物的挑战和未来方向.

科学领域:

  • 制药科学 制药科学
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 传统药物发现面临重大障碍,包括高成本,长时间和低成功率.
  • 人工智能 (AI) 和机器学习 (ML) 为克服这些挑战提供了有希望的解决方案.
  • 最近的进展 (2019-2024) 显示了AI/ML在药物发现管道中的潜力.

研究的目的:

  • 在药物发现 (2019-2024) 中批判性地分析最近的AI/ML方法.
  • 在关键阶段检查人工智能应用:目标识别,领先发现,优化和安全评估.
  • 确定挑战,并提出人工智能在制药研发中的整合未来方向.

主要方法:

  • 关于AI/ML在药物发现方面的进展的综合文献综述.
  • 分析各种人工智能技术:深度学习,图形神经网络,变压器.
  • 对人工智能方法的比较评估,重点关注数据质量,验证和道德.

主要成果:

  • 人工智能/ML技术在加速药物发现的各个阶段显示出显著的前景.
  • 在目标识别,生成和临床前安全方面确定了关键应用.
  • 限制包括数据可访问性,模型可解释性和临床翻译挑战.

更多相关视频

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
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Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish
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相关实验视频

Last Updated: Sep 18, 2025

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

9.7K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

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Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish
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Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish

Published on: July 16, 2012

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结论:

  • 人工智能/肌痛性脑脊髓炎的整合为开发更安全,更有效的药物提供了变革性的潜力.
  • 解决数据,可解释性和验证方面的挑战对于成功实施至关重要.
  • 未来的努力应专注于透明的方法和道德框架,以实现负责任的人工智能采用.