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相关概念视频

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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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...
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Drug Discovery: Overview01:26

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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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G Protein-coupled Receptors01:15

G Protein-coupled Receptors

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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Targets for Drug Action: Overview01:26

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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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最近的深度学习应用在基于结构的药物设计中

Jacob Verburgt1, Anika Jain1, Daisuke Kihara2,3

  • 1Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

Methods in molecular biology (Clifton, N.J.)
|September 7, 2023
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概括
此摘要是机器生成的。

深度学习模型通过增强识别和优化与蛋白质结合的小分子的计算方法来彻底改变药物发现. 本概述对药物开发应用的近期深度学习进展进行了分类.

关键词:
在 Ab initio 分子生成过程中.计算分子表示的分子表示.计算机辅助药物设计深度学习是一种深度学习.生成对抗性网络 (GANs) 是一种产生对抗性的网络.领导优化优化 领导优化连接体姿势的生成过程.连接体的姿势得分得分.药物动力学优化 药物动力学优化基于结构的药物设计.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 医学中的人工智能

背景情况:

  • 小分子的识别和优化对于早期药物开发至关重要.
  • 计算模型长期以来一直有助于预测分子结合亲和力.
  • 传统的计算方法正在被深度学习方法所增强.

研究的目的:

  • 为提供最近基于深度学习的药物发现发展的概述.
  • 根据其在药物发现任务中的应用,对深度学习方法进行分类.
  • 讨论每个子类别内的一般框架和个别方法.

主要方法:

  • 关于在药物发现中深度学习应用的最新文献的综述.
  • 将深度学习方法分为四个依赖任务的子类别.
  • 对每个子类别的一般框架和特定算法的分析.

主要成果:

  • 深度学习模型显示了改善计算药物发现的巨大潜力.
  • 介绍了用于各种药物发现任务的深度学习方法的结构化分类.
  • 讨论了关键的深度学习方法及其应用.

结论:

  • 深度学习是推动计算药物发现的强大工具.
  • 提出的分类有助于理解和应用深度学习方法.
  • 预计深度学习的进一步整合将加速新疗法的发展.