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相关概念视频

The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
12.9K
Ligand Binding Sites02:40

Ligand Binding Sites

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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...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.8K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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基准测试主动学习协议用于干结合亲和力预测.

Rohan Gorantla1,2,3, Alžbeta Kubincová3, Benjamin Suutari3

  • 1School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K.

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|March 6, 2024
PubMed
概括
此摘要是机器生成的。

积极学习 (AL) 通过系统评估机器学习模型和参数来优化药物发现. 高斯过程模型在稀疏数据方面表现出色,而更大的初始批量大小可以改善顶部粘合剂识别.

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科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学 化学信息学
  • 机器学习在药物发现中的作用

背景情况:

  • 积极学习 (AL) 对于从大型分子图书馆中识别强效药物候选者至关重要.
  • 了解AL参数的影响对于设计有效的药物发现协议至关重要.

研究的目的:

  • 系统地评估机器学习模型,样本选择和批量大小在药物发现的积极学习中.
  • 评估数据集特征和噪声对AL性能的影响.

主要方法:

  • 利用四个亲和数据集 (TYK2,USP7,D2R,Mpro) 来测试高斯过程 (GP) 和Chemprop模型.
  • 使用R2,斯皮尔曼等级,RMSE,召回和F1得分等指标评估性能.
  • 评估了初始和随后的批量大小和人工数据噪声的影响.

主要成果:

  • 在稀疏的数据集上,GP模型的性能优于Chemprop;在较大的数据集上,性能相当.
  • 较大的初始批量大小改善了顶部粘合剂识别 (回忆) 和整体相关性.
  • 较小的批量大小 (20-30个化合物) 是随后的AL周期的最佳选择.
  • 适度的人工噪音没有妨碍,但过度的噪音会对性能产生负面影响.

结论:

  • 选择AL模型和参数显著影响药物发现结果.
  • 优化批量大小和考虑数据稀疏性对于强大的主动学习协议至关重要.
  • 即使在适度的数据噪声下,AL仍然有效,但过度的噪声会损害其实用性.