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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
53
Data Validation01:15

Data Validation

162
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
162
Response Surface Methodology01:16

Response Surface Methodology

129
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
129
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

127
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...
127
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

71
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.
71
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

40
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
40

You might also read

Related Articles

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

Sort by
Same author

Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026
Same author

Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization.

Journal of chemical information and modeling·2026
Same author

Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models.

Journal of chemical information and modeling·2026
Same author

A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026
Same author

Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2026
Same author

Machine Learning Models Predicting Solubility and Polymerizability of Polyimides Considering Multiple Monomers for CO<sub>2</sub> Separation Membranes.

Molecular informatics·2026

Related Experiment Video

Updated: Jun 30, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

555

Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the

Hiromasa Kaneko1

  • 1Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.

ACS Omega
|March 18, 2024
PubMed
Summary

This study introduces a novel method to optimize the applicability domain (AD) model selection for mathematical models. It identifies the best AD method and hyperparameters for diverse datasets, ensuring reliable predictions in molecular and material design.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jun 30, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

555
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • * Cheminformatics and computational chemistry.
  • * Materials science and engineering.
  • * Data science and machine learning.

Background:

  • * Establishing the applicability domain (AD) is crucial for the reliable use of mathematical models in design and control.
  • * Existing AD methods have numerous hyperparameters, necessitating a systematic approach for selection.
  • There is a lack of standardized methods for optimizing AD models for specific datasets and mathematical models.

Purpose of the Study:

  • * To propose and validate a novel method for evaluating and optimizing applicability domain (AD) models.
  • * To enable the selection of the most appropriate AD method and hyperparameters for diverse datasets and mathematical models.
  • To enhance the reliability of predictions in molecular, material, and process design.

Main Methods:

  • * Calculation of the relationship between coverage and root-mean-squared error (RMSE) using double cross-validation predictions.
  • * Computation of the area under the coverage and RMSE curve (AUCR) for all AD method and hyperparameter combinations.
  • Selection of the AD model with the lowest AUCR as the optimal fit for the mathematical model.

Main Results:

  • * The proposed method successfully generated optimal AD models across eight diverse datasets (molecules, materials, spectra).
  • * Validation confirmed the method's ability to identify the best AD model and hyperparameters for each specific dataset and mathematical model.
  • Demonstrated improved reliability in defining the scope of model predictions.

Conclusions:

  • * The developed method provides a robust framework for optimizing applicability domain (AD) models.
  • * This optimization is essential for ensuring the accuracy and reliability of predictive models in scientific and engineering applications.
  • The Python code is publicly available, facilitating broader adoption and further research.