Jove
Visualize
Contact Us

Related Concept Videos

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.6K
VSEPR Theory for Determination of Electron Pair Geometries
46.6K
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

14.5K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
14.5K
Molecular Orbital Theory II03:51

Molecular Orbital Theory II

28.0K
Molecular Orbital Energy Diagrams
28.0K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

11.2K
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,...
11.2K
Molecular Orbital Theory I02:35

Molecular Orbital Theory I

48.4K
Overview of Molecular Orbital Theory
48.4K
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

65.7K
Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
65.7K

You might also read

Related Articles

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

Sort by
Same author

A Noniterative Supervised On-Chip Training Circuitry for Reservoir Computing Systems.

IEEE transactions on neural networks and learning systems·2022
Same author

Fully Parallel Stochastic Computing Hardware Implementation of Convolutional Neural Networks for Edge Computing Applications.

IEEE transactions on neural networks and learning systems·2022
Same author

Climate change and their impacts in the Balearic Islands: a guide for policy design in Mediterranean regions.

Regional environmental change·2021
Same author

A global multinational survey of cefotaxime-resistant coliforms in urban wastewater treatment plants.

Environment international·2020
Same author

Compact Hardware Synthesis of Stochastic Spiking Neural Networks.

International journal of neural systems·2019
Same author

An improved methodology to compute surface site interaction points using high density molecular electrostatic potential surfaces.

Journal of computational chemistry·2018
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 Experiment Video

Updated: Mar 6, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

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

Published on: February 23, 2024

3.8K

A Bayesian Target Predictor Method based on Molecular Pairing Energies estimation.

Antoni Oliver1, Vincent Canals1, Josep L Rosselló1

  • 1Physics Department, Universitat de les Illes Balears, Palma de Mallorca, Spain.

Scientific Reports
|March 7, 2017
PubMed
Summary

This study introduces a novel compact energy-based model for efficient virtual screening and drug target prediction. The new method significantly improves the identification of active compounds in large databases.

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

2.7K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

70.0K

Related Experiment Videos

Last Updated: Mar 6, 2026

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

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

Published on: February 23, 2024

3.8K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

2.7K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

70.0K

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Virtual screening (VS) is crucial for early drug discovery, requiring efficient molecular models for large database analysis.
  • Accurate and rapid target prediction is essential for identifying potential drug candidates.

Purpose of the Study:

  • To develop and validate a new compact energy-based model for virtual screening and drug target prediction.
  • To enhance the speed and accuracy of identifying compounds likely to bind to drug targets.

Main Methods:

  • A novel compact energy-based model utilizing smart energy vectors to represent molecular polar regions and their geometry.
  • Application of the model for similarity searches within the Directory of Useful Decoys (DUD) database.
  • Development of a Bayesian Classifier for target prediction, using active compounds to build energy-dependent probability distribution functions.

Main Results:

  • The proposed model demonstrated superior performance compared to previously published models in virtual screening.
  • The model effectively estimates molecule pairing energies for rapid identification of active compounds.
  • The Bayesian Classifier provides an effective approach for energy-dependent target prediction.

Conclusions:

  • The new compact energy-based model offers a significant advancement for virtual screening and drug target prediction.
  • This approach enables faster and more accurate identification of potential drug candidates from extensive molecular databases.
  • The methodology holds promise for accelerating the early stages of drug discovery.