sMolBoxes: Dataflow Model for Molecular Dynamics Exploration
Pavol Ulbrich, Manuela Waldner, Katar\'ina Furmanov\'a, S\'ergio M., Marques, David Bedn\'a\v{r}, Barbora Kozlikova, Jan By\v{s}ka

TL;DR
sMolBoxes introduces a dataflow model that links quantitative property analysis with interactive 3D visualizations, enabling efficient exploration of large molecular dynamics simulations beyond frame-by-frame observation.
Contribution
The paper presents a novel node-based dataflow approach for molecular dynamics analysis that integrates quantitative properties with visual exploration, improving efficiency over traditional scripting methods.
Findings
Enables exploration of millions of MD snapshots efficiently.
Facilitates hypothesis generation through progressive analytics.
Perceived as more efficient than scripting-based analysis.
Abstract
We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable…
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Taxonomy
TopicsScientific Computing and Data Management · Microbial Metabolic Engineering and Bioproduction · Protein Structure and Dynamics
