Jove
Visualize
Contact Us

Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.5K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

915
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
915
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

321
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...
321

You might also read

Related Articles

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

Sort by
Same author

Hydrogel Electronic Skin Synergistically Enhanced by Multibond Crosslinking and a Negative Poisson's Ratio Structure for Human Respiration Monitoring.

ACS sensors·2026
Same author

Ceramic Glazing to Enhance Stability and Selectivity of CoAl<sub>2</sub>O<sub>4</sub> Catalyst for Wastewater Advanced Oxidation.

Environmental science & technology·2026
Same author

Spin Channels Enable •H-Triggered Ozone Activation for Self-Accelerating Degradation of Reduced-Sulfur Pollutant.

Angewandte Chemie (International ed. in English)·2026
Same author

Atomic engineering of electronic metal-support interaction via Si-O-Co bonding for sustainable catalytic ozone purification.

Journal of colloid and interface science·2026
Same author

Reinforcing Covalency via d-p-d Orbital Coupling Enables Dual-Site Selective Ozone Activation for Efficient CH<sub>3</sub>SH Mineralization.

Environmental science & technology·2025
Same author

Reactive Halogen Species Boost Fungal Spore Inactivation in Seawater during UV/PMS Treatment.

Environmental science & technology·2025
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 Experiment Video

Updated: Mar 29, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K

A High-Performance and Interpretable pKa Prediction Framework Integrating Count-Based Fingerprints and Ensemble

Hui Shen1, Yongquan He2, Juefeng Deng2

  • 1Zhejiang Key Laboratory of Digital Intelligence Monitoring and Restoration of Watershed Environment, College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China.

Molecules (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

This study introduces a new machine learning model using count-based Morgan fingerprints for accurate acid dissociation constant (pKa) prediction. The model demonstrates strong generalizability and interpretability, aiding environmental risk assessments.

Keywords:
applicability domaincount-based Morgan fingerprintmachine learningpKa predictionquantitative structure-property relationship

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

8.1K

Related Experiment Videos

Last Updated: Mar 29, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K
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

8.1K

Area of Science:

  • Computational chemistry
  • Environmental science
  • Machine learning

Background:

  • Accurate prediction of acid dissociation constant (pKa) is crucial for understanding organic compound environmental fate.
  • Traditional binary Morgan fingerprints (B-MF) lack stoichiometric information, hindering accurate pKa modeling.
  • Substituent effects significantly influence pKa, necessitating improved predictive methods.

Purpose of the Study:

  • Develop an interpretable machine learning framework for high-performance pKa prediction.
  • Integrate count-based Morgan fingerprints (C-MF) to capture functional group multiplicity.
  • Enhance pKa prediction accuracy and generalizability for environmental applications.

Main Methods:

  • Developed a machine learning framework using count-based Morgan fingerprints (C-MF) and ensemble algorithms.
  • Employed SHAP-based recursive feature elimination (SHAP-RFE) for model optimization.
  • Defined the applicability domain using the AD_SAL method for reliable predictions.

Main Results:

  • Count-based Morgan fingerprints (C-MF) outperformed traditional binary Morgan fingerprints (B-MF).
  • The optimized Catboost model achieved high accuracy (test-set R² = 0.890, RMSE = 1.026).
  • SHAP analysis confirmed chemically intuitive feature importance and model interpretability.
  • External validation demonstrated strong generalizability with R² = 0.890 and RMSE = 0.942.

Conclusions:

  • The developed framework provides a robust and generalizable tool for accurate pKa prediction.
  • The model's interpretability facilitates understanding of substituent effects on pKa.
  • This approach has significant potential for environmental risk assessment and chemical safety evaluations.