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

56
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...
56
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

85
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
85
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

131
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...
131
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

703
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
703
Solubility Equilibria: Overview01:09

Solubility Equilibria: Overview

680
When a substance such as sodium chloride is added to water, it dissolves, forming an aqueous solution. The extent of dissolution is called solubility. The process of dissolution can exist in equilibrium, just like other chemical processes. Solubility equilibria are also called precipitation equilibria because the process of solubility can be reversible. The reverse of the solubility process is called precipitation.
Solubility is important in biological and environmental processes. A notable...
680
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

309
Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the...
309

You might also read

Related Articles

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

Sort by
Same author

Catalytic Asymmetric Hydration of Alkenes.

Journal of the American Chemical Society·2026
Same author

Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks.

ACS central science·2026
Same author

Current Insights on Skin Permeability Data and Quantitative Structure-Property Relationship Modeling.

Molecular informatics·2026
Same author

Interpretable and Scalable Similarity Metrics for DNA-Encoded Library Design Using Generative Topographic Mapping.

Molecular informatics·2026
Same author

Toward Reaction Vessel Mimicry: Machine Learning-Assisted Automated Exploration of Alkene Polymerization and Its Transferability.

Journal of chemical theory and computation·2026
Same author

Membrane-associated effluxosomes coordinate multi-metal resistance in Mycobacterium tuberculosis.

The EMBO journal·2026
Same journal

SpaceExpander: An Automated System for Drafting Markush Claims to Expand Chemical Space.

Molecular informatics·2026
Same journal

A Structure-Informed Atlas of Venom-Derived Peptides Reveals the Organization of Chemical Space.

Molecular informatics·2026
Same journal

ConGen: Targeted Molecule Generation Through Contrastive Learning and Latent Optimization.

Molecular informatics·2026
Same journal

Novel Molecules Generation Using Graph Generative Adversarial Networks.

Molecular informatics·2026
Same journal

An Attention-Driven Graph Transformer With Nonlinear Modeling and Neuro-Fuzzy Fusion for High-Order Toxic Molecular Graph Learning.

Molecular informatics·2026
Same journal

Molecular Modeling and Chemoinformatics in Ukraine.

Molecular informatics·2026
See all related articles

Related Experiment Video

Updated: Jul 7, 2025

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

9.0K

Kinetic solubility: Experimental and machine-learning modeling perspectives.

Shamkhal Baybekov1, Pierre Llompart1,2, Gilles Marcou1

  • 1Laboratoire de Chémoinformatique UMR 7140 CNRS, Institut Le Bel, University of Strasbourg, 4 Rue Blaise Pascal, 67081, Strasbourg, France.

Molecular Informatics
|December 27, 2023
PubMed
Summary
This summary is machine-generated.

This study shows that kinetic solubility assays are reproducible and can be modeled. Predictive models for kinetic solubility are essential as it cannot be substituted by thermodynamic solubility in drug discovery.

Keywords:
QSPRcomparisonkinetic solubilitythermodynamic solubility

More Related Videos

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.5K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K

Related Experiment Videos

Last Updated: Jul 7, 2025

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

9.0K
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.5K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Kinetic solubility is crucial for high-throughput screening in early drug discovery.
  • Kinetic solubility assays often exhibit low reproducibility due to protocol sensitivity.
  • Limited efforts exist in developing Quantitative Structure-Property Relationship (QSPR) models for kinetic solubility compared to thermodynamic solubility.

Purpose of the Study:

  • To investigate the reproducibility and modelability of kinetic solubility assays.
  • To analyze the relationship between kinetic and thermodynamic solubility.
  • To assess the consistency of data across different kinetic solubility assays.

Main Methods:

  • Analysis of the correlation between kinetic and thermodynamic solubility data.
  • Examination of data consistency from various kinetic solubility assays.
  • Development and evaluation of QSPR models for kinetic solubility using merged datasets.

Main Results:

  • Observed differences between kinetic and thermodynamic solubility align with existing literature.
  • Demonstrated good agreement between data from different kinetic solubility campaigns, contrary to expectations.
  • Achieved high-performing QSPR models for kinetic solubility using combined datasets.
  • Showcased poor performance of thermodynamic solubility QSPR models on kinetic solubility data, confirming their lack of correlation.

Conclusions:

  • Kinetic and thermodynamic solubilities are distinct properties and cannot be used interchangeably.
  • Kinetic solubility assays exhibit better reproducibility than commonly assumed.
  • The development of predictive QSPR models for kinetic solubility is feasible and encouraged.
  • A freely accessible kinetic solubility QSPR model is available via the Predictor web service.