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Related Concept Videos

Introduction to Scalers01:21

Introduction to Scalers

Many familiar physical quantities can be specified completely by giving a single number and the appropriate unit. For example, "a class period lasts 50 min," or "the gas tank in my car holds 65 L," or "the distance between the two posts is 100 m." A physical quantity that can be specified completely in this manner is called a scalar quantity. The word "scalar" is a synonym for "number." Time, mass, distance, length, volume, temperature, and energy are some examples of scalar quantities.
Scalar...
Maximizing the Directional Derivative01:25

Maximizing the Directional Derivative

The directional derivative is a central concept in multivariable calculus that describes how a function changes at a given point when moving in a specified direction. This direction is represented by a unit vector, ensuring that only the orientation influences the rate of change. By varying the direction, different rates of change can be observed, demonstrating that the directional derivative depends strongly on the chosen direction.The directional derivative is computed using the gradient...
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Mass Analyzers: Overview

The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
Downsampling01:20

Downsampling

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Fineness Modulus01:19

Fineness Modulus

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Interval Level of Measurement

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Related Experiment Video

Updated: Jul 6, 2026

Analysis and Specification of Starch Granule Size Distributions
08:46

Analysis and Specification of Starch Granule Size Distributions

Published on: March 4, 2021

Size-intensive descriptors.

George D Purvis1

  • 1Fujitsu Computer Systems, Biosciences Group, 15244 NW Greenbrier Pkwy, Beaverton, OR, 97006, USA. gpurvis@us.fujitsu.com

Journal of Computer-Aided Molecular Design
|March 22, 2008
PubMed
Summary

New size-intensive descriptors, created by dividing chemical descriptors by sample size, improve quantitative structure-property relationship (QSPR) models. These intrinsic descriptors are independent of sample size and frequently outperform their original counterparts in predictive accuracy.

Area of Science:

  • * Quantitative Structure-Property Relationships (QSPR)
  • * Cheminformatics
  • * Computational Chemistry

Background:

  • * Quantitative Structure-Property Relationship (QSPR) models often incorporate non-linear effects using mathematical transformations of descriptors.
  • * Cross-dependencies between descriptors are sometimes addressed using product terms.
  • * Existing methods for handling non-linearities and cross-dependencies can be complex.

Purpose of the Study:

  • * To introduce and evaluate a novel class of descriptors: size-intensive descriptors.
  • * To demonstrate the utility of size-intensive descriptors in improving QSPR model performance.
  • * To explore the relationship between original descriptors and their size-intensive counterparts.

Main Methods:

Related Experiment Videos

Last Updated: Jul 6, 2026

Analysis and Specification of Starch Granule Size Distributions
08:46

Analysis and Specification of Starch Granule Size Distributions

Published on: March 4, 2021

  • * Generation of size-intensive descriptors by dividing standard descriptors (e.g., molecular weight, volume) by chemical sample size.
  • * Development of automated QSPR models incorporating both extensive and size-intensive descriptors.
  • * Analysis of descriptor correlations and model performance metrics (e.g., r^2).

    • Main Results:

    • * Size-intensive descriptors are independent of the chemical sample size and exhibit weak correlation with their original extensive descriptors.
    • * Automated QSPR models frequently select size-intensive descriptors as the best predictors, leading to higher model accuracy (r^2).
    • * Examples of QSPR models demonstrating the effectiveness of size-intensive descriptors are provided.

    Conclusions:

    • * Size-intensive descriptors offer a simple yet effective method to enhance QSPR models by capturing size-independent properties.
    • * These descriptors provide a valuable alternative to complex transformations for addressing non-linearities and cross-dependencies.
    • * The physical significance and predictive power of size-intensive descriptors warrant their broader application in QSPR studies.