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Related Concept Videos

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Reliability and Validity01:29

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects or...
Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
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Dose-Response Relationship: Selectivity and Specificity01:25

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Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and β2-adrenergic receptors...

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Advancing Dyslexia Assessment in Children Through Computerized Testing
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Published on: August 16, 2024

Selectivity data: assessment, predictions, concordance, and implications.

Cen Gao1, Suntara Cahya, Christos A Nicolaou

  • 1Discovery Chemistry, ‡Discovery Statistics, §Advanced Analytics, Lilly Research Laboratories, Eli Lilly and Company , Indianapolis, Indiana 46285, United States.

Journal of Medicinal Chemistry
|August 14, 2013
PubMed
Summary
This summary is machine-generated.

In silico predictions show agreement with experimental data in drug discovery, but experimental results are more consistent for highly selective molecules. Bias correction is crucial for accurate selectivity assessments.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Pharmacology

Background:

  • In silico methods are increasingly used in drug discovery.
  • Evaluating the agreement between predictive and experimental data is essential.
  • Existing metrics may not fully capture concordance across diverse data sources.

Purpose of the Study:

  • To introduce a new metric, concordance minimum significant ratio (cMSR), for evaluating data agreement.
  • To assess the concordance between in silico predictions and experimental data in drug discovery.
  • To investigate factors influencing the reliability of computational models.

Main Methods:

  • Development and application of the concordance minimum significant ratio (cMSR) metric.
  • Analysis of selectivity data from multiple experimental and predictive sources.
  • Evaluation of computational model performance across different datasets.

Main Results:

  • The overall agreement between predicted and experimental data is comparable to inter-experimental agreement.
  • Concordance is significantly higher between experimental sources than between experimental and predicted values for potent/selective molecules.
  • Computational models show limited predictivity across different data sources, emphasizing the need for bias correction.

Conclusions:

  • In silico predictions show promise but do not yet fully replace experimental testing for highly selective or potent compounds.
  • Bias correction is vital for accurate assessment of small-molecule selectivity.
  • While general trends in target space are similar, details differ significantly between data sources and models.