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相关概念视频

Systematic Sampling Method01:17

Systematic Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
319
Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Updated: Jul 4, 2025

Optimization of the Ugi Reaction Using Parallel Synthesis and Automated Liquid Handling
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Optimization of the Ugi Reaction Using Parallel Synthesis and Automated Liquid Handling

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普森采样――一种有效的方法来搜索需求数据库上的超大合成.

Kathryn Klarich1, Brian Goldman2, Trevor Kramer2

  • 1ReNAgade Therapeutics, 640 Memorial Drive, Cambridge, Massachusetts 02139, United States.

Journal of chemical information and modeling
|February 5, 2024
PubMed
概括
此摘要是机器生成的。

普森采样 (TS) 通过有效选大量分子库来加速药物发现. 这种主动学习方法可以更快地识别顶级候选药物,从而减少识别成功的成本和时间.

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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
  • 机器学习 机器学习

背景情况:

  • 超大合成按需库的虚拟选对于药物发现中的成功识别至关重要.
  • 化学库 (数十亿个分子) 的指数增长使得彻底的虚拟选成本低效.
  • 需要启发式搜索方法来有效地从广的化学空间中识别有希望的分子.

研究的目的:

  • 引入和展示普森采样 (TS) 作为一个积极的学习方法来简化虚拟选.
  • 展示TS在试剂空间中执行概率搜索的能力,避免完整的库列举.
  • 为了说明TS在各种虚拟选模式中的广泛适用性.

主要方法:

  • 普森采样 (TS) 是一种主动学习算法,用于概率虚拟选.
  • 该TS方法应用于一个大型组合图书馆的基于对接的虚拟屏幕.
  • 该方法的有效性在识别排名最高的分子,与详尽的选相比,被评估.

主要成果:

  • TS成功地从3.35亿个分子数据集中识别出超过50%的前100个分子.
  • 这一成就是通过评估总数据集的仅1%来实现的,这表明了显著的计算节省.
  • 该研究证实了TS在各种虚拟选应用中的有效性,包括相似性搜索和机器学习模型.

结论:

  • 普森抽样提供了一个计算效率高且有效的策略,用于药物发现中的成功识别.
  • TS显著减少了对超大型化学图书馆进行虚拟选所需的资源.
  • 这种主动学习方法代表了管理和搜索药物开发的大型分子数据库的范式转变.