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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.6K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.6K
Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.1K
The Two-State Receptor Model01:29

The Two-State Receptor Model

1.9K
The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
1.9K
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

56
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
56
Drug Discovery: Overview01:26

Drug Discovery: Overview

7.3K
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...
7.3K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

474
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...
474

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相关实验视频

Updated: May 24, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

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以知识为导向的扩散模型用于3D联体-药对象映射.

Jun-Lin Yu1, Cong Zhou1, Xiang-Li Ning1

  • 1Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.

Nature communications
|March 6, 2025
PubMed
概括
此摘要是机器生成的。

DiffPhore是一个新的AI框架,通过准确预测3D连接体-药理相映射来增强药物发现. 这种方法可以改善虚拟查,并识别新药候选药物,在药物研究中推进人工智能.

更多相关视频

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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175
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

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

Published on: February 23, 2024

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相关实验视频

Last Updated: May 24, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.2K
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

175
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

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

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科学领域:

  • 计算化学是一种计算化学.
  • 人工智能在药物发现中的作用
  • 制药类型的建模.

背景情况:

  • 药理对药物发现至关重要,但深度学习的整合仍然有限.
  • 现有的工具往往难以准确地绘制3D连接体-药理相的映射.
  • 推进人工智能驱动的药方法对于有效的药物开发至关重要.

研究的目的:

  • 推出 DiffPhore,一个以知识为导向的扩散框架,用于"随时"的3D连接物-药理相映射.
  • 为了提高预测连接体结合形状的准确性和效率.
  • 加强虚拟选能力,以发现和目标捕捞.

主要方法:

  • 开发了一个以知识为导向的扩散框架 (DiffPhore) 用于配体 - 药相映射.
  • 利用了联体-药匹配知识来指导联体构造的生成.
  • 采用校准采样,以解决代性形状搜索中的暴露偏差.
  • 在两个定制数据集上训练了模型,这些数据集是3D连接体-药对.

主要成果:

  • 在预测连接体结合形状方面,DiffPhore取得了最先进的性能.
  • 性能优于传统的药工具和先进的对接方法.
  • 证明了卓越的虚拟选能力,用于发现和目标捕捞.
  • 成功确定了具有验证的结合模式的人类谷氨基基环酶的新型抑制剂.

结论:

  • DiffPhore代表了人工智能支持的药导向药物发现的重大进步.
  • 该框架为识别潜在的候选药物提供了更高的准确性和效率.
  • 这项工作为更广泛地采用基于药理的药物设计中的深度学习铺平了道路.