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相关概念视频

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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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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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MOKPE:通过多元优化基于内核保存嵌入的药物向相互作用预测.

Oğuz C Binatlı1, Mehmet Gönen2,3

  • 1Graduate School of Sciences and Engineering, Koç University, 34450, Istanbul, Turkey.

BMC bioinformatics
|July 5, 2023
PubMed
概括

本研究介绍了基于多重优化的内核保存嵌入 (MOKPE) 进行药物向相互作用 (DTI) 预测. MOKPE有效地模拟异质数据,在预测DTI方面表现优于现有方法.

关键词:
药物重新定位是药物重新定位.药物目标相互作用预测预测.核心方法 核心方法机器学习 机器学习多重优化多重优化的优化

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 生物信息学往往涉及整合来自不同,异构来源的数据.
  • 确定药物向相互作用 (DTI) 对药物发现至关重要.
  • 现有的方法在模拟复杂,异构的数据以进行DTI预测方面遇到了困难.

研究的目的:

  • 提出一个新的框架,基于多重优化的内核保护嵌入 (MOKPE),用于模拟异质数据.
  • 通过保持数据类型内部和数据类型之间的相似性,有效地预测药物向相互作用.
  • 在药物发现中提高DTI识别的准确性和效率.

主要方法:

  • 开发了基于多重优化的内核保护嵌入 (MOKPE) 框架.
  • 预测异质药物和目标数据到一个统一的嵌入空间.
  • 同时保留药物-标相互作用,药物-药物相似性和标-标相似性.

主要成果:

  • 与基于相似性的最先进方法相比,MOKPE在预测DTI方面表现优越或可比.
  • 通过对四个DTI网络数据集进行十倍交叉验证的十次复制进行评估.
  • 成功预测了以前未见的DTI,并对预测给定网络内的未知DTI进行了评估.

结论:

  • MOKPE是一种有效的框架,用于模拟生物信息学中的异质数据.
  • 拟议的方法显著提高了DTI预测的准确性和效率.
  • R实现和复制脚本是公开可用的,以便进一步研究.