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

Molecules with Multiple Chiral Centers02:25

Molecules with Multiple Chiral Centers

11.5K
Molecules that possess multiple chiral centers can afford a large number of stereoisomers. For instance, while some molecules like 2-butanol have one chiral center, defined as a tetrahedral carbon atom with four different substituents attached, several molecules like butane-2,3-diol have multiple chiral centers. A simple formula to predict the number of stereoisomers possible for a molecule with n chiral centers is 2n. However, there can be a lower number where some of the stereoisomers are...
11.5K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.2K
VSEPR Theory for Determination of Electron Pair Geometries
34.2K
Molecular Models02:00

Molecular Models

38.2K
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.
38.2K
Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

629
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...
629
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
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...
48
Molecular Geometry and Dipole Moments02:36

Molecular Geometry and Dipole Moments

12.9K
The VSEPR theory can be used to determine the electron pair geometries and molecular structures as follows:
12.9K

You might also read

Related Articles

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

Sort by
Same author

A review of deep learning approaches for drug synergy prediction in cancer.

npj drug discovery·2026
Same author

Multimodal Information-Driven Heterogeneous Graph Neural Networks for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling·2026
Same author

Transcending Structural Dependencies: A Tunable Mass Spectrometry-Driven Machine Learning Framework for Genotoxicity Prediction.

Environmental science & technology·2026
Same author

Pathogenicity prediction for noncanonical splice-altering variants based on multimodal feature fusion.

Briefings in bioinformatics·2026
Same author

iDualG4: A Dual-Channel Deep Learning Framework for Predicting In Vivo G-Quadruplexes.

Biomolecules·2026
Same author

Intelligent methods in bioinformatics and genomics.

Methods (San Diego, Calif.)·2026
Same journal

Correction to "AstraMEV (AI-Guided Structural Assembly of Multi-Epitope Vaccines) Against Infectious Bronchitis Virus".

Journal of chemical information and modeling·2026
Same journal

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations.

Journal of chemical information and modeling·2026
Same journal

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026
Same journal

Structural and Thermodynamic Discrimination between Agonists and Antagonists of Retinoic Acid Receptor γ and the Vitamin D Receptor.

Journal of chemical information and modeling·2026
Same journal

PACEff Builder: An Efficient Platform for Constructing PACE Hybrid-Resolution Models for Molecular Dynamics Simulations of Aqueous Protein, Peptide Assembly, and Membrane Protein Systems.

Journal of chemical information and modeling·2026
Same journal

TransKla: A Local-Global Cross-Attention Based Transformer Approach for Prediction of Lysine Lactylation Sites.

Journal of chemical information and modeling·2026
See all related articles

Related Experiment Video

Updated: Jun 24, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

Evolutionary Multiobjective Molecule Optimization in an Implicit Chemical Space.

Xin Xia1,2, Yiping Liu3, Chunhou Zheng1

  • 1The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, China.

Journal of Chemical Information and Modeling
|June 13, 2024
PubMed
Summary
This summary is machine-generated.

We developed a new multiobjective molecule optimization framework (MOMO) to design drug molecules with multiple optimized properties. MOMO generates diverse, novel, and high-property molecules, outperforming existing methods in drug discovery.

More Related Videos

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.2K

Related Experiment Videos

Last Updated: Jun 24, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.2K

Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Bioinformatics

Background:

  • Generative methods are crucial for designing optimized molecules in drug development.
  • Current methods struggle to simultaneously optimize multiple drug properties while ensuring diversity and novelty.

Purpose of the Study:

  • To introduce a novel multiobjective molecule optimization framework (MOMO).
  • To address the limitations of existing methods in generating diverse, novel, and high-property molecules with multiple optimized attributes.

Main Methods:

  • MOMO utilizes a Pareto-based multiproperty evaluation strategy at the molecular sequence level.
  • An evolutionary search is guided within an implicit chemical space.

Main Results:

  • MOMO significantly outperforms five state-of-the-art methods in diversity, novelty, and optimized properties on benchmark tasks.
  • Validated practical applicability on four real-world drug discovery challenges.

Conclusions:

  • MOMO offers a robust solution for molecule optimization problems involving multiple properties.
  • The framework can serve as a valuable tool to accelerate drug discovery and development.