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Design Example: Application of Archimedes' Principle01:11

Design Example: Application of Archimedes' Principle

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Archimedes' principle is fundamental in analyzing the buoyant force and stability of floating bodies. In this example, a wooden block with a rectangular section floats in seawater. Based on the block's dimensions, its specific gravity and the specific weight of seawater are used to find the volume of water displaced and the center of buoyancy.
The volume of seawater displaced by the block is determined by first calculating the block's weight. This is done by multiplying the...
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Precipitation Titration: Endpoint Detection Methods01:19

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In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
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Factorial Design02:01

Factorial Design

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Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Design Example: Designing a Residential Plumbing System01:25

Design Example: Designing a Residential Plumbing System

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The design of residential plumbing systems requires carefully evaluating water demand, flow rates, and pressure dynamics to ensure both efficiency and reliability. The nature of water flow within pipes is defined by its Reynolds number, which classifies flow as either laminar (smooth) or turbulent.
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Design Example: Designing Water Slide01:18

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When designing a water slide, controlling the speed of water flow is crucial for rider safety while maintaining an exciting experience. As water flows down the slide, gravity causes it to accelerate, with its speed at the bottom depending on the height from which it starts. The higher the slide, the more potential energy the water has at the top, which is converted into kinetic energy as it descends, increasing its speed.
Bernoulli's principle determines the water's velocity along the slide....
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Related Experiment Video

Updated: Feb 1, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints.

Alexios Koutsoukas1, George Chang1, Christopher E Keefer1

  • 1Computational ADME Group, Department of Pharmacokinetics, Dynamics, and Metabolism , Pfizer Worldwide Research & Development , Groton , Connecticut 06340 , United States.

Journal of Chemical Information and Modeling
|December 1, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces MMPA-by-QSAR, a novel method combining quantitative structure-activity relationship (QSAR) models with matched molecular pair analysis (MMPA). This approach effectively predicts chemical transformation effects on lipophilicity and metabolic clearance, unlocking new design ideas for drug discovery.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Matched Molecular Pair Analysis (MMPA) is a powerful tool for extracting knowledge from chemical databases to aid lead optimization.
  • Traditional MMPA is limited by the scarcity of measured data for many chemical transformations, hindering the discovery of novel design strategies.
  • Existing methods often fail to leverage the vast potential of underutilized chemical transformations due to data limitations.

Purpose of the Study:

  • To develop and validate a quantitative structure-activity relationship (QSAR) model-augmented MMPA approach (MMPA-by-QSAR).
  • To infer the impact of chemical transformations on lipophilicity and metabolic clearance.
  • To enable the exploration of infrequent or novel chemical transformations beyond existing measured data.

Main Methods:

  • Employing QSAR models to predict compound activities and properties.
  • Utilizing MMPA to identify and extract virtual trends from predicted data.
  • Retrospectively analyzing lipophilicity (SFLogD) and metabolic clearance (HLM) endpoints.
  • Prospectively applying MMPA-by-QSAR to identify novel transformations in untested compounds.

Main Results:

  • MMPA-by-QSAR accurately predicted the magnitude of change for lipophilicity and metabolic clearance transformations.
  • Retrospective analyses showed high prediction accuracy for SFLogD (up to 99%) and HLM (up to 99.5%).
  • Prospective application identified novel transformations, with predicted directionality confirmed experimentally for most cases.

Conclusions:

  • MMPA-by-QSAR significantly expands the utility of MMPA by enabling the analysis of virtual chemical transformations.
  • This approach can uncover novel and infrequent molecular modifications, accelerating drug design and optimization.
  • The method holds potential for identifying new design strategies where measured data is scarce.