Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

36.1K
VSEPR Theory for Determination of Electron Pair Geometries
36.1K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

228
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
228
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

112
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
112
Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

838
Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
Titrations between an acid and a base lead to neutralization reactions that form...
838
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

126
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
126
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

150
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
150

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Barriers and Facilitating Factors for Preoperative Non-Invasive Positive Pressure Ventilation Compliance in Chinese Patients with Obesity Hypoventilation Syndrome: A Qualitative Study.

Patient preference and adherence·2026
Same author

Integrating whole-genome bisulfite sequencing and TCGA data reveals methylation patterns associated with the MTHFR 677 C > T Variant.

Scientific reports·2026
Same author

Prediction of physicochemical properties of organic compounds using degree-based topological indices and machine learning models.

Scientific reports·2026
Same author

Quantitative proteomic analysis was performed to evaluate the protein expression levels in the ovaries of goats with different litter sizes.

BMC genomics·2026
Same author

Degree-Based Topological Indices and Machine Learning for QSPR Modeling of Arthritis Drugs.

ACS omega·2026
Same author

The CaM1-CBP60b-MYB77 Transcriptional Cascade Regulates K<sup>+</sup> Homeostasis and Salt Tolerance in Barley.

Plant biotechnology journal·2026

Related Experiment Video

Updated: Sep 13, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.7K

Predicting flavonoid physicochemical properties using topological indices and regression modeling.

Huili Li1,2, Shamaila Yousaf3, Komal Shahzadi3

  • 1School of Software, Pingdingshan University, Pingdingshan, 467000, China.

Scientific Reports
|July 29, 2025
PubMed
Summary

This study uses topological indices to predict flavonoid properties, finding quadratic models best for estimating molar refractivity, molar volume, and vaporization enthalpy. This aids in cost-effective drug discovery by prioritizing bioactive compounds.

Keywords:
Biological systemsComputational modelingFlavonoidsPhysicochemical propertiesTopological indices

More Related Videos

Biosynthesis of a Flavonol from a Flavanone by Establishing a One-pot Bienzymatic Cascade
09:50

Biosynthesis of a Flavonol from a Flavanone by Establishing a One-pot Bienzymatic Cascade

Published on: August 14, 2019

9.4K
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

2.2K

Related Experiment Videos

Last Updated: Sep 13, 2025

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

10.7K
Biosynthesis of a Flavonol from a Flavanone by Establishing a One-pot Bienzymatic Cascade
09:50

Biosynthesis of a Flavonol from a Flavanone by Establishing a One-pot Bienzymatic Cascade

Published on: August 14, 2019

9.4K
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

2.2K

Area of Science:

  • Cheminformatics and Quantitative Structure-Property Relationship (QSPR) studies.
  • Phytochemical analysis and drug discovery.
  • Computational chemistry and molecular modeling.

Background:

  • Flavonoids are vital polyphenolic phytochemicals with diverse biological activities.
  • Predicting physicochemical properties is crucial for understanding and utilizing these compounds.
  • Topological indices offer a computational approach to molecular characterization.

Purpose of the Study:

  • To predict six physicochemical properties of sixty flavonoids using degree-based topological indices.
  • To evaluate the performance of linear, quadratic, and logarithmic regression models for property prediction.
  • To establish a cost-effective method for prioritizing bioactive flavonoids in drug discovery.

Main Methods:

  • Utilized degree-based topological indices (TIs) to represent molecular structures.
  • Employed linear, quadratic, and logarithmic regression models for prediction.
  • Validated model performance using correlation coefficients (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE).

Main Results:

  • Quadratic regression models demonstrated superior predictive power for molar refractivity, molar volume, and enthalpy of vaporization.
  • High statistical agreement was observed between predicted and experimental values for external compounds.
  • Nonlinear relationships were identified between topological indices and the studied physicochemical properties.

Conclusions:

  • The developed QSPR methodology provides a reliable and cost-effective tool for rapid property estimation of flavonoids.
  • This approach facilitates the prioritization of promising flavonoid candidates in drug discovery pipelines.
  • The study highlights the utility of topological indices in bridging cheminformatics and biological applications for polyphenolic systems.