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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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Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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Viral Structure00:56

Viral Structure

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Viruses are extraordinarily diverse in shape and size, but they all have several structural features in common. All viruses have a core that contains a DNA- or RNA-based genome. The core is surrounded by a protective coat of proteins called the capsid. The capsid is composed of subunits called capsomeres. The capsid and genome-containing core are together known as the nucleocapsid.
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相关实验视频

Updated: Jul 13, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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基于结构的虚拟选机器学习评分的实用指南.

Viet-Khoa Tran-Nguyen1, Muhammad Junaid1, Saw Simeon1

  • 1Centre de Recherche en Cancérologie de Marseille, Marseille, France.

Nature protocols
|October 16, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于构建和评估基于结构的虚拟选 (SBVS) 的特定目标机器学习评分函数 (SF) 的协议. 这种方法增强了对治疗点的活性分子的发现.

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科学领域:

  • 计算化学的计算化学
  • 药物发现 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 基于结构的虚拟查 (SBVS) 使用对接来识别治疗点的活性分子.
  • 随着生物活性信息的大量化学和蛋白质数据集的可用性显著增加.
  • 机器学习 (ML),包括深度学习,有效地利用这些数据集来开发SBVS的评分函数 (SF).

研究的目的:

  • 为SBVS提供一个全面和用户友好的协议,用于构建和评估针对特定目标的基于ML的SF.
  • 为增强SBVS性能提供关于数据增强,算法选择和SF优化的实际指导.
  • 为了促进发现具有较高活动可能性的分子对特定目标的发现.

主要方法:

  • 该协议涉及四个关键步骤:在公共基准上评估通用SF,准备特定目标的实验数据,为ML建模分区数据,并生成/评估特定目标的MLSF.
  • 使用公共存储库来获取数据,并为ML建模准备培训和测试数据集.
  • 使用可访问的软件 (Smina,CNN-Score,RF-Score-VS,DeepCoy) 和用于SF生成和评估的网络资源.

主要成果:

  • 该协议使用三个示例目标来演示:乙胆化酶,HMG-CoA减少酶和过氧酶增殖器激活受体-α.
  • 所有必要的代码和数据都是公开可用的,使得可复制性和应用.
  • 该过程可以在一周内在一台计算机上执行,突出其效率.

结论:

  • 针对特定目标的基于ML的SFs代表了SBVS的最先进的状态,通常表现优于通用方法.
  • 提出的协议提供了通过增强训练数据和选择适当的ML算法来优化SBVS的实际指导.
  • 该协议的成功实施可以显著提高发现新疗法剂的效率和成功率.