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相关概念视频

Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

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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...
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Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

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An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
Antagonists can be classified as competitive or noncompetitive based on their...
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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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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...
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Drug Discovery: Overview01:26

Drug Discovery: Overview

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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...
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Drug-Receptor Interactions01:29

Drug-Receptor Interactions

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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....
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相关实验视频

Updated: Jun 14, 2025

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

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DTI-LM:语言模型驱动的药物向相互作用预测.

Khandakar Tanvir Ahmed1,2, Md Istiaq Ansari1,2, Wei Zhang1,2

  • 1Department of Computer Science, University of Central Florida, Orlando, FL 32816, United States.

Bioinformatics (Oxford, England)
|September 2, 2024
PubMed
概括

使用语言模型的新框架DTI-LM改善了药物向相互作用的预测,特别是对于新蛋白质. 它利用序列数据和邻里信息来提高药物发现的准确性.

科学领域:

  • 计算生物学是一种计算生物学.
  • 药物的发现和开发.
  • 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) 对于药物发现至关重要.
  • 基于序列的计算模型提供了高效的DTI预测.
  • 对于未知药物或蛋白质的冷启动DTI预测仍然是一个挑战.

研究的目的:

  • 引入DTI-LM,这是一个用于DTI预测的新型框架.
  • 利用预先训练的语言模型和邻里信息来增强DTI预测.
  • 为了弥合热启动和冷启动之间的差距,DTI使用序列表示进行预测.

主要方法:

  • 利用先进的预训练语言模型来对药物和蛋白质进行序列表示.
  • 通过图形注意力网络集成邻里信息.
  • 开发了DTI-LM框架,仅依赖于序列数据.

主要成果:

  • 在四个数据集中,DTI-LM在DTI预测任务上取得了最先进的性能.
  • 对蛋白质的冷启动预测有显著的改进.
  • 观察到蛋白质和药物之间冷启动预测的持续差异.

更多相关视频

Diagonal Method to Measure Synergy Among Any Number of Drugs
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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Diagonal Method to Measure Synergy Among Any Number of Drugs

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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

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结论:

  • DTI-LM有效地使用基于序列的语言模型预测药物向相互作用.
  • 该框架在解决DTI预测中的冷启动挑战方面显示出前景.
  • 需要进一步的研究来解决药物和蛋白质冷启动预测中观察到的差异.