Mol* web molecular graphics engine

Web-based molecular graphics have transformed the interactive visualization of molecular data, leveraging modern web technologies that provide GPU acceleration, optimized JavaScript engines, and seamless access across devices without additional software installation. We present the graphics engine at the core of the Mol* toolkit, a high-performance, open-source framework that is widely adopted in academia and industry, including by the Protein Data Bank, UniProt, EMDB, and AlphaFold DB. The engine combines a comprehensive styling system with a suite of optimized rendering primitives, including real-time surface generation, to deliver both flexibility and visual fidelity. Efficient handling of large-scale molecular scenes is achieved through level-of-detail management, GPU instancing, spatial acceleration structures, and frustum/occlusion culling. A screen-space global illumination model provides scalable, high-quality lighting, while integrated AR/VR support enables immersive molecular exploration. Together, these capabilities enable engaging, real-time, high-fidelity visualization of molecular systems, across a wide range of scales, from single atoms to billion-atom mesoscale assemblies, demonstrating the strengths of a bespoke web-native rendering engine for molecular graphics, available at https://molstar.org.

Authors

Institutions

Publication Details

Journal
Protein Science
Published
2026-03-12
DOI
https://doi.org/10.1002/pro.70514
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mol* web molecular graphics engine

David Sehnal, Á. Kovács, Ludovic Autin, Gianluca Tomasello et al.
Protein Science
Scientific Computing and Data Management
article

Mol* web molecular graphics engine

David Sehnal, Á. Kovács, Ludovic Autin, Gianluca Tomasello, Alexander S. Rose
article en

Abstract

Web-based molecular graphics have transformed the interactive visualization of molecular data, leveraging modern web technologies that provide GPU acceleration, optimized JavaScript engines, and seamless access across devices without additional software installation. We present the graphics engine at the core of the Mol* toolkit, a high-performance, open-source framework that is widely adopted in academia and industry, including by the Protein Data Bank, UniProt, EMDB, and AlphaFold DB. The engine combines a comprehensive styling system with a suite of optimized rendering primitives, including real-time surface generation, to deliver both flexibility and visual fidelity. Efficient handling of large-scale molecular scenes is achieved through level-of-detail management, GPU instancing, spatial acceleration structures, and frustum/occlusion culling. A screen-space global illumination model provides scalable, high-quality lighting, while integrated AR/VR support enables immersive molecular exploration. Together, these capabilities enable engaging, real-time, high-fidelity visualization of molecular systems, across a wide range of scales, from single atoms to billion-atom mesoscale assemblies, demonstrating the strengths of a bespoke web-native rendering engine for molecular graphics, available at https://molstar.org.

Protein ScienceVol. 35(4)
Scripps Research Institute (US), European Bioinformatics Institute (GB), TU Wien (AT), San Diego Supercomputer Center (US), Masaryk University (CZ), AGF Electronics (Italy) (IT), Bioinformatics Institute (RU)
Industry, innovation and infrastructure
Openalex Percentile: Top 23%
Scientific Computing and Data Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.