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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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.
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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...
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Random Sampling Method

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...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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Reaction Quotient02:35

Reaction Quotient

The status of a reversible reaction is conveniently assessed by evaluating its reaction quotient (Q). For a reversible reaction described by m A + n B ⇌ x C + y D, the reaction quotient is derived directly from the stoichiometry of the balanced equation as

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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y-Randomization and its variants in QSPR/QSAR.

Christoph Rücker1, Gerta Rücker, Markus Meringer

  • 1Biozentrum, University of Basel, 4056 Basel, Switzerland. christoph.ruecker@uni-bayreuth.de

Journal of Chemical Information and Modeling
|September 21, 2007
PubMed
Summary

Y-randomization is a validation tool for quantitative structure-activity relationship (QSAR) models. This study compares y-randomization with variants using pseudodescriptors, highlighting differences due to descriptor intercorrelation and proposing a double-test approach for robust model validation.

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Area of Science:

  • * Cheminformatics
  • * Computational Chemistry
  • * Quantitative Structure-Activity Relationships (QSAR)

Background:

  • * Y-randomization is a standard technique for validating QSPR/QSAR models by comparing model performance against randomly shuffled response variables.
  • * Existing methods may not fully account for descriptor intercorrelation, potentially leading to an overestimation of model robustness.
  • * Accessibility of descriptor data can limit the application of traditional y-randomization.

Purpose of the Study:

  • * To compare the efficacy of y-randomization and its variants in QSPR/QSAR model validation.
  • * To investigate the impact of using original versus random number pseudodescriptors on validation outcomes.
  • * To propose an improved validation strategy that addresses limitations of existing methods.

Main Methods:

  • * Comparative analysis of y-randomization and novel variants using multilinear regression (MLR) with descriptor selection.
  • * Implementation of experiments using original response, permuted response, and pseudo-random number response variables.
  • * Utilization of both original and random number pseudodescriptors in model building simulations.

Main Results:

  • * Variants using random number pseudodescriptors yielded higher mean random r2 values compared to y-randomization due to descriptor intercorrelation.
  • * A double-test approach comparing original model r2 against both types of random validation is proposed.
  • * Application to published MLR QSAR equations identified instances of model failure.

Conclusions:

  • * The proposed double-test enhances QSPR/QSAR model validation by considering descriptor intercorrelation.
  • * Random number experiments offer a viable alternative when full descriptor data for y-randomization is unavailable.
  • * The study provides a more rigorous framework for assessing the reliability of QSPR/QSAR models.