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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.

Noncovalent Attractions in Biomolecules

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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.
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Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...

Noncovalent Attractions in Biomolecules

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Updated: Nov 16, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

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Published on: September 1, 2023

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field.

Tamar Schlick^1,2,3, Stephanie Portillo-Ledesma¹, Christopher G Myers¹

¹Department of Chemistry, New York University, New York, New York 10003, USA;

Annual Review of Biophysics

|February 19, 2021

Summary

This summary is machine-generated.

Biomolecular modeling and simulation have made significant strides, exceeding long-term expectations. Advances in computational power, algorithms like machine learning, and force fields enhance accuracy and scope, fostering strong experimental collaborations.

Keywords:

DNA folding RNA folding artificial intelligence biomolecular dynamics biomolecular modeling biomolecular simulation citizen science projects machine learning multiscale modeling protein folding structure prediction

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Last Updated: Nov 16, 2025

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Modeling an Enzyme Active Site using Molecular Visualization Freeware

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

Area of Science:

Computational Biology
Biophysics
Biochemistry

Background:

Reassesses progress in biomolecular modeling and simulation since 2011.
Highlights the field's productive phase, with underestimated long-term impacts.

Purpose of the Study:

To evaluate the productivity and successes of biomolecular modeling and simulation.
To discuss the impact of computational advances and collaborative efforts.

Main Methods:

Review of productivity metrics and success examples in biomolecular modeling.
Analysis of advancements in computer power, force fields, and algorithms (AI/ML).

Main Results:

Overestimated short-term expectations but underestimated long-term effects.
Successful prediction of biomolecular structures and mechanisms.
Enhanced accuracy and scope of modeling and simulation.

Conclusions:

Biomolecular modeling and simulation is a thriving discipline.
Strong partnerships between modeling/simulation and experimentation are established.
Machine learning and AI are key drivers of progress.