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

Molecular Models

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.

Inductive Effects on Chemical Shift: Overview

Inductive Effects on Chemical Shift: Overview

The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Chemical Shift: Internal References and Solvent Effects

Chemical Shift: Internal References and Solvent Effects

In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

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...

Experimental Determination of Chemical Formula

Experimental Determination of Chemical Formula

The elemental makeup of a compound defines its chemical identity, and chemical formulas are the most concise way of representing this elemental makeup. When a compound’s formula is unknown, measuring the mass of its constituent elements is often the first step in determining the formula experimentally.

You might also read

Related Articles

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

Sort by

Same author

Chain-Mobility-Enabled 1D Lanthanide Coordination Polymer Glassy Scintillators for Underwater X-Ray Videography.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Computational Identification of Active Drug Metabolites for Human Protein Targets.

Molecular pharmaceutics·2026

Same author

EC-Dock: A Fast Equivariant Consistency Model for Molecular Docking and Virtual Screening.

Journal of chemical information and modeling·2026

Same author

Stimuli-Responsive Triplet Emission and X-Ray Scintillation via Reversible Structural Switching in Pyromellitic Diimide Cocrystals.

Angewandte Chemie (International ed. in English)·2026

Same author

A Return to the Logic-First Spirit of Corey's Retrosynthetic Analysis, Now Implemented in Modern Data-Driven CASP.

ACS central science·2026

Same author

Integrating Machine Learning Interatomic Potentials with MMPBSA for Accurate Protein-Ligand Binding Free Energy Calculations.

The journal of physical chemistry. B·2026

Search research articles

Related Experiment Video

Updated: Nov 5, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Comparative Study of Deep Generative Models on Chemical Space Coverage.

Jie Zhang^1,2,3, Rocío Mercado⁴, Ola Engkvist⁴

¹Guangdong Provincial Key Laboratory of Laboratory Animals, Guangdong Laboratory Animals Monitoring Institute, Guangzhou 510663, P. R. China.

Journal of Chemical Information and Modeling

|May 21, 2021

Summary

This study introduces a new metric to evaluate deep generative models for de novo molecular design. The metric assesses chemical space coverage, differentiating model performance and generalization capabilities.

More Related Videos

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

Related Experiment Videos

Last Updated: Nov 5, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

Area of Science:

Computational chemistry
Machine learning
Drug discovery

Background:

Deep molecular generative models are advancing de novo molecular design.
Existing evaluation metrics often fail to assess chemical space coverage.
Deep learning architectures like RNNs, VAEs, and GANs are used for molecular generation.

Purpose of the Study:

To present a novel metric for evaluating deep molecular generative models.
To assess the chemical space coverage of generated molecules.
To compare the performance of different molecular generative models.

Main Methods:

Developed a new metric based on chemical space coverage of the GDB-13 dataset.
Trained seven molecular generative models using a small fraction of GDB-13.
Evaluated models by their ability to reproduce structures, ring systems, and functional groups from the reference set.

Main Results:

Significant variations in performance were observed among the seven generative models.
The new metric effectively differentiated the generalization capabilities of the models.
Comparative analysis of GDB-13 ring system and functional group coverages was performed.

Conclusions:

The proposed metric offers a valuable tool for evaluating and comparing deep molecular generative models.
This metric enhances the assessment of chemical space coverage, a critical aspect of molecular generation.
The findings highlight the importance of robust evaluation metrics in advancing de novo molecular design.