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Related Concept Videos

Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)00:53

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...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Analyte Adsorption and Distribution01:09

Analyte Adsorption and Distribution

In certain chromatographic separations, solutes transfer between the mobile phase and the stationary phase via sorption, which typically refers to the process of adsorption. For many chromatographic systems, the sorption process often depends on the polarity of the compounds—an expression of the overall dipole moment within the molecule. During the separation process, there is competition between the solute and solvent for adsorption to the stationary phase. Highly polar compounds and solvents...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: May 27, 2026

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ADMET-vault: an interactive framework for real-time ADMET prediction and molecular optimization.

Rishabh Vishwakarma1, Vini Lokhande1, Parveen Punia2

  • 1Department of Research & Development, Growdea Technologies Pvt. Ltd, Gurugram, Haryana, 122004, India.

Journal of Computer-Aided Molecular Design
|May 25, 2026
PubMed
Summary

ADMET-Vault is a new AI platform that predicts drug absorption, distribution, metabolism, excretion, and toxicity (ADMET) during early molecular design. This interactive tool helps optimize drug candidates, improving the drug discovery pipeline.

Keywords:
ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity)Interactive predictionMachine Learning (ML)Molecular optimizationPharmacokineticsTDC (Therapeutics Data Commons)

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning

Background:

  • Drug discovery faces challenges due to poor absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of candidates in clinical trials.
  • Early-stage ADMET profiling is crucial for successful drug development but often lacking in current workflows.

Purpose of the Study:

  • To present ADMET-Vault, an interactive machine learning platform for early-stage ADMET prediction and molecular design.
  • To integrate ADMET prediction into the drug design process, enabling real-time assessment and optimization of drug candidates.

Main Methods:

  • ADMET-Vault integrates diverse molecular representations: physicochemical descriptors, molecular fingerprints, and graph neural network embeddings.
  • The platform utilizes scaffold-based validation on 12 benchmark datasets from Therapeutics Data Commons.
  • It combines predictive machine learning models with a live molecular editor for interactive design-predict-optimize cycles.

Main Results:

  • ADMET-Vault demonstrated consistent predictive performance across multiple ADMET endpoints, with strong results for clearance, intestinal absorption, hepatotoxicity, and solubility.
  • Regression tasks showed mean absolute errors from 0.28 to 7.68 and Spearman's rank correlation coefficients from 0.44 to 0.66.
  • Classification tasks achieved area under the receiver operating characteristic curve values between 0.87 and 0.99.

Conclusions:

  • ADMET-Vault effectively integrates predictive accuracy with interactive molecular editing, filling a critical gap in early drug discovery.
  • The platform enables researchers to instantly assess the impact of structural changes on drug-like properties.
  • This interactive approach facilitates a more efficient and optimized drug design process, potentially reducing late-stage failures.