Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)

Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)

Acyclic diene metathesis polymerization or ADMET polymerization involves cross-metathesis of terminal dienes, such as 1,8-nonadiene, to give linear unsaturated polymer and ethylene. As ADMET is a reversible process, the formed ethylene gas must be removed from the reaction mixture to complete the polymerization process.
Similar to cross-metathesis, ADMET also involves the formation of metallacyclobutane intermediate by [2+2] cycloaddition of one of the double bonds of a terminal diene with...

You might also read

Related Articles

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

Sort by

Same author

Prelnc2: A prediction tool for lncRNAs with enhanced multi-level features of RNAs.

PloS one·2023

Same author

Changes in the VOC of Fruits at Different Refrigeration Stages of 'Ruixue' and the Participation of Carboxylesterase <i>MdCXE20</i> in the Catabolism of Volatile Esters.

Foods (Basel, Switzerland)·2023

Same author

Synthesis of Aminoalkyl Sclareolide Derivatives and Antifungal Activity Studies.

Molecules (Basel, Switzerland)·2023

Same author

Filamentous Fungi-Derived Orsellinic Acid-Sesquiterpene Meroterpenoids: Fungal Sources, Chemical Structures, Bioactivities, and Biosynthesis.

Planta medica·2023

Same author

Automatic Thoughts, Self-Stigma, and Resilience Among Schizophrenia Patients with Metabolic Syndrome: A Cross-Sectional Study.

Neuropsychiatric disease and treatment·2023

Same author

Effects of femtosecond laser-assisted minimally invasive lamellar keratoplasty (FL-MILK) on mild-to-moderate and advanced keratoconus.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2023

Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026

Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 14, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Machine Learning for In Silico ADMET Prediction.

Lei Jia¹, Hua Gao²

¹Bristol Myers Squibb, San Diego, CA, USA. leijiachem@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)

|November 3, 2021

Summary

This summary is machine-generated.

Predicting drug properties like absorption, distribution, metabolism, excretion, and toxicity (ADMET) is crucial for drug development. Machine learning and in silico methods aid in assessing these properties early, improving drug efficacy and safety.

Keywords:

ADMET Cubist Deep Neural Networks Descriptors Machine Learning Prediction

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Oct 14, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Area of Science:

Pharmacology and Toxicology
Computational Chemistry
Drug Discovery

Background:

Drug candidate efficacy and safety are critical for successful clinical development.
High attrition rates in new chemical entity development are often linked to unforeseen efficacy and safety issues.
Understanding a drug molecule's pharmacokinetic and pharmacodynamic properties, collectively known as ADMET, is essential.

Purpose of the Study:

To highlight the importance of ADMET profiling in early-stage drug discovery.
To review the application of machine learning and QSAR methods in ADMET modeling.
To emphasize recent advancements in in silico prediction of ADMET properties.

Main Methods:

Utilizing machine learning algorithms for ADMET prediction.
Employing quantitative structure-activity relationship (QSAR) models.
Leveraging advanced in silico techniques for early assessment of bioactive compounds.

Main Results:

Machine learning and QSAR methods have demonstrated success in modeling ADMET properties.
Recent progress includes enhanced data collection for improved predictive accuracy.
In silico methods are increasingly effective in predicting ADMET profiles during early drug development.

Conclusions:

Accurate ADMET profiling is vital for reducing clinical attrition.
In silico approaches offer powerful tools for predicting drug behavior and safety.
Continued development in computational methods will accelerate the discovery of safer and more effective drugs.