The evolving landscape of molecular visualization

Molecular visualization plays a central role in structural biology, transforming data into representations that reveal how molecular form relates to function. Since the construction of the first protein models in the 1950s, visualization practices have evolved in tandem with experimental and computational advances, shaping both research and communication. Today's scientists rely on an expanding suite of digital tools to interpret structural, biophysical, and imaging data, while public repositories facilitate dissemination and education. Yet, as experimental methods capture ever more complex and dynamic molecular systems, the limitations of static visualizations have become apparent. Recent progress in animation, integrative modeling, and artificial intelligence offers new possibilities for representing molecular complexity and motion. This review traces the evolution of molecular visualization from physical models to dynamic, data-integrated animations and explores how emerging technologies promise to make visualization not only a medium of communication but also a tool for scientific exploration.

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Publication Details

Journal
Protein Science
Published
2026-04-11
DOI
https://doi.org/10.1002/pro.70560
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.00

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article

The evolving landscape of molecular visualization

Dillon Lee, Janet Iwasa, Rachel M. Torrez
Protein Science
Data Visualization and Analytics
article

The evolving landscape of molecular visualization

Dillon Lee, Janet Iwasa, Rachel M. Torrez
article en

Abstract

Molecular visualization plays a central role in structural biology, transforming data into representations that reveal how molecular form relates to function. Since the construction of the first protein models in the 1950s, visualization practices have evolved in tandem with experimental and computational advances, shaping both research and communication. Today's scientists rely on an expanding suite of digital tools to interpret structural, biophysical, and imaging data, while public repositories facilitate dissemination and education. Yet, as experimental methods capture ever more complex and dynamic molecular systems, the limitations of static visualizations have become apparent. Recent progress in animation, integrative modeling, and artificial intelligence offers new possibilities for representing molecular complexity and motion. This review traces the evolution of molecular visualization from physical models to dynamic, data-integrated animations and explores how emerging technologies promise to make visualization not only a medium of communication but also a tool for scientific exploration.

Protein ScienceVol. 35(5)
University of Utah (US)
National Institutes of Health
Quality Education
Openalex Percentile: Top 20%
Data Visualization and Analytics
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