Molecular simulations and visualization: introduction and overview

Here we provide an introduction and overview of current progress in the field of molecular simulation and visualization, touching on the following topics: (1) virtual and augmented reality for immersive molecular simulations; (2) advanced visualization and visual analytic techniques; (3) new developments in high performance computing; and (4) applications and model building

Physics-based visual characterization of molecular interaction forces

Molecular simulations are used in many areas of biotechnology, such as drug design and enzyme engineering. Despite the development of automatic computational protocols, analysis of molecular interactions is still a major aspect where human comprehension and intuition are key to accelerate, analyze, and propose modifications to the molecule of interest. Most visualization algorithms help the users by providing an accurate depiction of the spatial arrangement: the atoms involved in inter-molecular contacts. There are few tools that provide visual information on the forces governing molecular docking. However, these tools, commonly restricted to close interaction between atoms, do not consider whole simulation paths, long-range distances and, importantly, do not provide visual cues for a quick and intuitive comprehension of the energy functions (modeling intermolecular interactions) involved. In this paper, we propose visualizations designed to enable the characterization of interaction forces by taking into account several relevant variables such as molecule-ligand distance and the energy function, which is essential to understand binding affinities. We put emphasis on mapping molecular docking paths obtained from Molecular Dynamics or Monte Carlo simulations, and provide time-dependent visualizations for different energy components and particle resolutions: atoms, groups or residues. The presented visualizations have the potential to support domain experts in a more efficient drug or enzyme design process.Peer ReviewedPostprint (author's final draft

ScaleTrotter: Illustrative Visual Travels Across Negative Scales

We present ScaleTrotter, a conceptual framework for an interactive, multi-scale visualization of biological mesoscale data and, specifically, genome data. ScaleTrotter allows viewers to smoothly transition from the nucleus of a cell to the atomistic composition of the DNA, while bridging several orders of magnitude in scale. The challenges in creating an interactive visualization of genome data are fundamentally different in several ways from those in other domains like astronomy that require a multi-scale representation as well. First, genome data has intertwined scale levels---the DNA is an extremely long, connected molecule that manifests itself at all scale levels. Second, elements of the DNA do not disappear as one zooms out---instead the scale levels at which they are observed group these elements differently. Third, we have detailed information and thus geometry for the entire dataset and for all scale levels, posing a challenge for interactive visual exploration. Finally, the conceptual scale levels for genome data are close in scale space, requiring us to find ways to visually embed a smaller scale into a coarser one. We address these challenges by creating a new multi-scale visualization concept. We use a scale-dependent camera model that controls the visual embedding of the scales into their respective parents, the rendering of a subset of the scale hierarchy, and the location, size, and scope of the view. In traversing the scales, ScaleTrotter is roaming between 2D and 3D visual representations that are depicted in integrated visuals. We discuss, specifically, how this form of multi-scale visualization follows from the specific characteristics of the genome data and describe its implementation. Finally, we discuss the implications of our work to the general illustrative depiction of multi-scale data

Visual analysis of protein-ligand interactions

The analysis of protein-ligand interactions is complex because of the many factors at play. Most current methods for visual analysis provide this information in the form of simple 2D plots, which, besides being quite space hungry, often encode a low number of different properties. In this paper we present a system for compact 2D visualization of molecular simulations. It purposely omits most spatial information and presents physical information associated to single molecular components and their pairwise interactions through a set of 2D InfoVis tools with coordinated views, suitable interaction, and focus+context techniques to analyze large amounts of data. The system provides a wide range of motifs for elements such as protein secondary structures or hydrogen bond networks, and a set of tools for their interactive inspection, both for a single simulation and for comparing two different simulations. As a result, the analysis of protein-ligand interactions of Molecular Simulation trajectories is greatly facilitated.Peer ReviewedPostprint (author's final draft

A general illumination model for molecular visualization

Several visual representations have been developed over the years to visualize molecular structures, and to enable a better understanding of their underlying chemical processes. Today, the most frequently used atom-based representations are the Space-filling, the Solvent Excluded Surface, the Balls-and-Sticks, and the Licorice models. While each of these representations has its individual benefits, when applied to large-scale models spatial arrangements can be difficult to interpret when employing current visualization techniques. In the past it has been shown that global illumination techniques improve the perception of molecular visualizations; unfortunately existing approaches are tailored towards a single visual representation. We propose a general illumination model for molecular visualization that is valid for different representations. With our illumination model, it becomes possible, for the first time, to achieve consistent illumination among all atom-based molecular representations. The proposed model can be further evaluated in real-time, as it employs an analytical solution to simulate diffuse light interactions between objects. To be able to derive such a solution for the rather complicated and diverse visual representations, we propose the use of regression analysis together with adapted parameter sampling strategies as well as shape parametrization guided sampling, which are applied to the geometric building blocks of the targeted visual representations. We will discuss the proposed sampling strategies, the derived illumination model, and demonstrate its capabilities when visualizing several dynamic molecules.Peer ReviewedPostprint (author's final draft

VOICE: Visual Oracle for Interaction, Conversation, and Explanation

We present VOICE, a novel approach for connecting large language models' (LLM) conversational capabilities with interactive exploratory visualization. VOICE introduces several innovative technical contributions that drive our conversational visualization framework. Our foundation is a pack-of-bots that can perform specific tasks, such as assigning tasks, extracting instructions, and generating coherent content. We employ fine-tuning and prompt engineering techniques to tailor bots' performance to their specific roles and accurately respond to user queries, and a new prompt-based iterative scene-tree generation establishes a coupling with a structural model. Our text-to-visualization method generates a flythrough sequence matching the content explanation. Finally, 3D natural language interaction provides capabilities to navigate and manipulate the 3D models in real-time. The VOICE framework can receive arbitrary voice commands from the user and responds verbally, tightly coupled with corresponding visual representation with low latency and high accuracy. We demonstrate the effectiveness and high generalizability potential of our approach by applying it to two distinct domains: analyzing three 3D molecular models with multi-scale and multi-instance attributes, and showcasing its effectiveness on a cartographic map visualization. A free copy of this paper and all supplemental materials are available at https://osf.io/g7fbr/

Interactive Visualization of Molecular Dynamics Simulation Data

Molecular Dynamics Simulations (MD) plays an essential role in the field of computational biology. The simulations produce extensive high-dimensional, spatio-temporal data describ-ing the motion of atoms and molecules. A central challenge in the field is the extraction and visualization of useful behavioral patterns from these simulations. Throughout this thesis, I collaborated with a computational biologist who works on Molecular Dynamics (MD) Simu-lation data. For the sake of exploration, I was provided with a large and complex membrane simulation. I contributed solutions to his data challenges by developing a set of novel visual-ization tools to help him get a better understanding of his simulation data. I employed both scientific and information visualization, and applied concepts of abstraction and dimensions projection in the proposed solutions. The first solution enables the user to interactively fil-ter and highlight dynamic and complex trajectory constituted by motions of molecules. The molecular dynamic trajectories are identified based on path length, edge length, curvature, and normalized curvature, and their combinations. The tool exploits new interactive visual-ization techniques and provides a combination of 2D-3D path rendering in a dual dimension representation to highlight differences arising from the 2D projection on a plane. The sec-ond solution introduces a novel abstract interaction space for Protein-Lipid interaction. The proposed solution addresses the challenge of visualizing complex, time-dependent interactions between protein and lipid molecules. It also proposes a fast GPU-based implementation that maps lipid-constituents involved in the interaction onto the abstract protein interaction space. I also introduced two abstract level-of-detail (LoD) representations with six levels of detail for lipid molecules and protein interaction. Finally, I proposed a novel framework consisting of four linked views: A time-dependent 3D view, a novel hybrid view, a clustering timeline, and a details-on-demand window. The framework exploits abstraction and projection to enable the user to study the molecular interaction and the behavior of the protein-protein interaction and clusters. I introduced a selection of visual designs to convey the behavior of protein-lipid interaction and protein-protein interaction through a unified coordinate system. Abstraction is used to present proteins in hybrid 2D space, and a projected tiled space is used to present both Protein-Lipid Interaction (PLI) and Protein-Protein Interaction (PPI) at the particle level in a heat-map style visual design. Glyphs are used to represent PPI at the molecular level. I coupled visually separable visual designs in a unified coordinate space. The result lets the user study both PLI and PPI separately, or together in a unified visual analysis framework

Spatial CPU-GPU data structures for interactive rendering of large particle data

In this work, I investigate the interactive visualization of arbitrarily large particle data sets which ft into system memory, but not into GPU memory. With conventional rendering techniques, interactivity of visualizations is drastically reduced when rendering tens- or hundreds of millions of objects. At the same time, graphics hardware memory capabilities limit the size of data sets which can be placed in GPU memory for rendering. To circumvent these obstacles, a progressive rendering approach is employed, which gradually streams and renders all particle data to the GPU without reducing or altering the particle data itself. The particle data is rendered according to a visibility sorting derived from occlusion relations between different parts of the data set, leading to a rendering order of scene contents guided by importance for the rendered image. I analyze and compare possible implementation choices for rendering particles as opaque spheres in OpenGL, which forms the basis of the particle rendering application developed within this work. The application utilizes a multi-threaded architecture, where data preprocessing on a CPU-thread and a rendering algorithm on a GPU-thread ensure that the user can interact with the application at any time. In particular it is guaranteed that the user can explore the particle data interactively, by ensuring minimal latency from user input to seeing the effects of that input. This is achieved by favoring user inputs over completeness of the rendered image at all stages during rendering. At the same time the user is provided with an immediate feedback about interactions by re-projecting all currently visible particles to the next rendered image. The re-projection is realized with an on-GPU particle-cache of visible particles that is built during particle data streaming and rendering, and drawn upon user interaction using the most recent camera confguration according to user inputs. The combination of the developed techniques allows interactive exploration of particle data sets with up to 1.5 billion particles on a commodity computer.In dieser Arbeit wird die interaktive Visualisierung beliebig großer Partikeldaten untersucht, wobei die Partikeldaten im Arbeitsspeicher hinterlegt sind, aber nicht zwangsläufig in den Grafikspeicher passen. Mit üblichen Rendering Methoden büßen Visualisierungen drastisch an Interaktivität ein, wenn mehrere zehn- bis hunderte Millionen Objekte dargestellt werden. Gleichzeitig ist die Größe möglicher zu visualisierender Datensätze begrenzt durch den Videospeicher von Grafikkarten, auf dem zu visualisierende Daten vorliegen müssen. Um diese Einschränkungen zu umgehen, wird in dieser Arbeit ein progressiver Rendering Ansatz verfolgt, der sukzessive alle Partikeldaten zur Grafikkarte hochlädt und rendert, ohne die Partikeldaten zu reduzieren oder anderweitig zu verändern. Die Partikeldaten werden entsprechend einer vorgenommenen Sichtbarkeitssortierung gerendert, die aus gegenseitigen Verdeckungen verschiedener Teile des Partikeldatensatzes berechnet wird. Dies führt dazu, dass Teile der Szene nach ihrer Wichtigkeit für das aktuelle Bild sortiert und dargestellt werden. Es werden verschiedene Möglichkeiten analysiert und verglichen, Partikel als opake Kugeln in OpenGL zu rendern. Dies formt die Grundlage für die Partikel-Rendering Software, die in dieser Arbeit entwickelt wurde. Die Architektur der Rendering-Software benutzt mehrere Threads, sodass durch eine Daten-Vorverarbeitung auf einem CPUThread und durch Rendering-Algorithmen auf einem GPU-Thread sichergestellt ist, dass der Benutzer mit der Software jederzeit interagieren kann. Insbesondere ist sichergestellt, dass der Benutzer die Partikeldaten interaktiv untersuchen kann, indem die Latenz zwischen Benutzereingaben und dem Anzeigen der daraus resultierenden Veränderungen minimal gehalten wird. Dies wird erreicht indem der Verarbeitung von Benutzereingaben an allen Stellen des Rendering-Prozesses höhere Priorität eingeräumt wird als der Vollständigkeit des gerenderten Bildes. Gleichzeitig wird dem Benutzer eine sofortige Rückmeldung über getätigte Benutzereingaben gegeben, indem alle sichtbaren Partikel in das nächste gerenderte Bild neu projeziert werden. Diese Neu-Projektion wird durch einen GPU-seitigen Partikel-Cache aller aktuell sichtbaren Partikel realisiert, der während des sukzessiven Partikelstreamings und -renderns aufgebaut wird. Sobald der Benutzer eine Eingabe tätigt, wird der auf der GPU liegende Partikel-Cache unter der aktuellsten benutzerdefinierten Kameraposition neu gerendert. Die Kombination dieser entwickelten Methoden erlaubt ein interaktives Betrachten von Partikeldaten mit bis zu 1,5 Milliarden Partikeln auf einem handelsüblichen Computer