existence of numerous representations for the same data can impose a
cognitive burden on the observer when visualizing the data trying to
mentally relate the different representations. In this sense, the
visualization task requires a visualization strategy to promote the
pertinent view point, highlighting the important visual information,
and streamlining the representation to ensure intuitive perception of
information without sacrificing its completeness. We believe that this is
one of the greatest challenges of scientific visualization in the present
context. One interesting perspective could be to make use of the new
technologies to remove some information from the visual channel - if
possible - and transfer it to another modality such as audio for instance
(Rau et al., 2015; Ballweg et al., 2016; Arbon et al., 2018), to reduce the
amount of remaining information on the visual channel. This reduced
visual channel information will be better understood and the cognitive
load will be more manageable. Nevertheless, it is interesting to consider
how the new generation of graphical representations may capture MD
features in a static image. This ties to the next challenge for future
graphical representations.

## 7.2 Extending the repertoire of representations

The challenge of extending the repertoire of representations for
visualizing trajectory data is often mentioned in the literature since
2005 (Goodsell, 2005). Improving the visualization of MD
trajectories of complex systems remains a relevant challenge,
because the simulations and the visualization of these data do
not evolve at the same rate. Indeed, we have observed that the
existing visualization techniques do not fully meet the needs of
molecular dynamics. Frame-by-frame visualization tools were
implemented based on classic representations. Also, levels of
abstraction or multiscale representations were proposed to allow
the visualization of complex data. However, the complexity of the
generated data is increasingly intertwined over long simulation
times. The existing graphic representations date from the 90 s
and multiscale representations may no longer be sufficient,
especially with the interactions and transformations that may
occur during molecular dynamics. Today, the challenge is to
design a new generation of graphic representations dedicated to
molecular dynamics. These representations should keep traces of the
dynamics as the trajectory is read. For example, representations
which, at time t of the trajectory, would allow the user to know what
occurred in previous frames could be very helpful for the modeler to
analyze and understand the biomolecule's behaviour. Thinking
about representations for dynamics by considering the
complexity of the data will make it possible to offer more
adequate tools for the visualization of these trajectories
representing physical phenomena in greater detail and enhancing
the perceptual and cognitive effectiveness of visual representations.

## 7.3 Addressing visualisation of ensembles of MD trajectories

In the same context of data complexity, the simultaneous analysis
and visualization of a set of molecular simulations is another challenge,
which has not been extensively addressed in the literature. Today, it

seems that addressing this challenge will be a crucial development to
handle upcoming datasets and requirements. Comparative visualization
becomes imperative in this context, facilitating the comprehension of
relationships and patterns across numerous simulations. Similarly, as
multiscale molecular dynamics simulations gain popularity, there is a
pressing need for the corresponding evolution of analysis and
visualization tools tailored to these simulations. One possible road-
map that has not been explored so far, is to connect visualisation tools
with AI-based companions, that could be trained to handle complex
selection queries involving multiple trajectory data, and provide both
analysis and interactive visualisation.

## 7.4 Technical challenges for efficient graphical rendering

Efficient graphical rendering requires massive computational
resources for large molecular systems. When dealing with larger and
more complex macromolecular structures, novel visualization
challenges arise in both rendering approaches and memory
management. The size of these structures may exceed the
capacity of GPU memory, and the data streaming from CPU
memory could be insufficient for real-time applications. The use
of streaming through pipelines from powerful machines is a solution
that could be considered to visualize large scale simulations. Parallel
rendering (or distributed rendering) is another eventual solution to
render frames faster. This makes it possible to manage the
complexity of the data and to achieve a high graphics rendering
quality, but it requires a powerful graphics server. Whereas modelers
already use HPC to generate the molecular dynamics simulations,
the access to these supercomputers is not unlimited. Thus, it will be
suitable to limit the use of such equipment for visualizing MD
simulations. *In-situ* approaches can be considered. They present a
potential for optimizing the use of supercomputers. Indeed, they
take advantage of “dead times” on the supercomputer during the
simulation to prepare visualizations and the analysis/reporting at the
end of the simulation (Vishwanath et al., 2011; Dreher and Raffin,
2014; Malakar et al., 2015; Dorier et al., 2016; Dirand et al., 2018).

## 7.5 The gap between advances in computer graphics and bioinformatics

Another challenge that comes up frequently in many reviews since
2011 and remains relevant is the gap between computer science
evolution and bioinformatics (Martinez et al., 2019b). Computer
scientists develop new concepts and graphical tools that improve
graphic rendering and reduce execution time. However, these
programs are often proof-of-concept demonstrators and may be
dedicated to other areas such as computer games. On the one hand,
bioinformaticians will not necessarily be aware of these novelties and
they may not have the computer graphics background to readapt these
solutions. To overcome this problem, it is important to create
collaborations between both communities. Participating at scientific
events bringing together structural biologists and researchers working
on computer graphics could be a good way to create collaborations that
are beneficial for researchers in both domains working in the field of
molecular visualization. Expanding the education of bioinformaticians
on the computer graphics concept and tools can allow readapting the graphics developments from different fields of application. Even if a novel and advantageous rendering algorithm is found and made available, turning it into a supported and sustainable tool poses an additional challenge.

## 7.6 The limitations of VR/AR/MR tools for MD visualization

As briefly mentioned in the software discussion, Section 6, the accessibility of VR headsets and the availability of VR packages on several MD simulation visualization softwares, permit the use of this technology to explore biological phenomena. Nevertheless, it is necessary that the existing software packages offer new navigation and 3D interaction tools adapted to the context of molecular dynamics to facilitate the manipulation of the molecular data respecting their physical and chemical properties. The major problem that could arise with the use of this technology is the size and complexity of the MD trajectory data being visualized. Many VR systems have limitations in terms of processing power, storage capacity, and display resolution, which impacts the quality and detail of the visualization. Additionally, the ability to interact with and manipulate the data in real-time is usually limited by the VR system's capability. To overcome these limitations, it may be necessary to use specialized VR systems that are optimized for scientific visualization and research and may for instance leverage cloud-based streaming approaches.

AR is currently less successful than VR in this field and represents an untapped potential. Yet AR is a good alternative for people who are not at ease with total immersion in a virtual environment, but it does not always offer 3D perception. However, in modeling, the important aspect is the perception of the 3D structure, so Mixed Reality (MR) should be a key technology for the future. It offers a 3D perception of the structure while maintaining contact with the real world. Combined with Spatial Computing, the ability of devices to be aware of their surroundings and to represent them digitally opens immense possibilities. These techniques will enable much-needed new avenues for visualization and interaction. However, they will face the same problem as VR in terms of data complexity. Finally, collaborative work interfaces should be more developed to allow coworkers to explore the same data from various viewpoints. This technology offers a new approach to explore MD simulations, for instance by visualizing the data from different perspectives thus reducing the problem of occlusion. In addition, VR, MR and SC technologies combined with the collaborative work can be a powerful tool for the dissemination of knowledge.

## 8 Conclusion

This report highlights scientific and technological advances in the visualization for complex molecular dynamics simulations. We acknowledge the potential of multiscale molecular representations, visual abstraction, and aggregation for a better understanding of molecular behavior and interactions, but also emphasize the urgent need for further exploration. Despite some new representations tailored to MD visualization, the field remains largely unexplored, especially with regard to the increasing dimensions

of simulated systems that exceed conventional visualization capabilities.

There is a need to re-evaluate existing representations and visualization techniques to identify and respond to the shortcomings they present in light of advancements in MD simulations. This includes not only the refinement of current methods, but also the development of new visualization metaphors for MD simulations. These new representations should aim to simplify both temporal and spatial scale complexity to reduce the cognitive burden and facilitate the identification of significant events within the simulations. Furthermore, it is important to bridge the gap between the scientific visualization of raw simulation data and the analytical visualization. By linking the scientific visualization of the raw simulation data with the visualization of the analysis data, we hope to effectively navigate the complexity of molecular dynamics and open up new areas of understanding.

## Author contributions

HB: Data curation, Investigation, Visualization, Writing—original draft, Writing—review and editing. MS: Writing—review and editing, Conceptualization. MD: Writing—original draft, Writing—review and editing. MB: Writing—original draft, Writing—review and editing, Supervision and Validation. JJ-P: Writing—original draft, Writing—review and editing, Supervision and Validation.

## Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the "Initiative d'Excellence" program from the French State, grants "DYNAMO," ANR-11-LABX-0011, and "CACSICE," ANR-11-EQPX-0008. We thank ANR for support of grants "PIRATE," ANR-21-CE45-0014 and "MINOMICS," ANR-19-CE45-0017. MB thanks Sesame Ile-de-France for co-funding the display wall used for testing some of the visualization works discussed in the review.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
on the computer graphics concept and tools can allow readapting the graphics developments from different fields of application. Even if a novel and advantageous rendering algorithm is found and made available, turning it into a supported and sustainable tool poses an additional challenge.

## 7.6 The limitations of VR/AR/MR tools for MD visualization

As briefly mentioned in the software discussion, Section 6, the accessibility of VR headsets and the availability of VR packages on several MD simulation visualization softwares, permit the use of this technology to explore biological phenomena. Nevertheless, it is necessary that the existing software packages offer new navigation and 3D interaction tools adapted to the context of molecular dynamics to facilitate the manipulation of the molecular data respecting their physical and chemical properties. The major problem that could arise with the use of this technology is the size and complexity of the MD trajectory data being visualized. Many VR systems have limitations in terms of processing power, storage capacity, and display resolution, which impacts the quality and detail of the visualization. Additionally, the ability to interact with and manipulate the data in real-time is usually limited by the VR system's capability. To overcome these limitations, it may be necessary to use specialized VR systems that are optimized for scientific visualization and research and may for instance leverage cloud-based streaming approaches.

AR is currently less successful than VR in this field and represents an untapped potential. Yet AR is a good alternative for people who are not at ease with total immersion in a virtual environment, but it does not always offer 3D perception. However, in modeling, the important aspect is the perception of the 3D structure, so Mixed Reality (MR) should be a key technology for the future. It offers a 3D perception of the structure while maintaining contact with the real world. Combined with Spatial Computing, the ability of devices to be aware of their surroundings and to represent them digitally opens immense possibilities. These techniques will enable much-needed new avenues for visualization and interaction. However, they will face the same problem as VR in terms of data complexity. Finally, collaborative work interfaces should be more developed to allow coworkers to explore the same data from various viewpoints. This technology offers a new approach to explore MD simulations, for instance by visualizing the data from different perspectives thus reducing the problem of occlusion. In addition, VR, MR and SC technologies combined with the collaborative work can be a powerful tool for the dissemination of knowledge.

## 8 Conclusion

This report highlights scientific and technological advances in the visualization for complex molecular dynamics simulations. We acknowledge the potential of multiscale molecular representations, visual abstraction, and aggregation for a better understanding of molecular behavior and interactions, but also emphasize the urgent need for further exploration. Despite some new representations tailored to MD visualization, the field remains largely unexplored, especially with regard to the increasing dimensions

of simulated systems that exceed conventional visualization capabilities.

There is a need to re-evaluate existing representations and visualization techniques to identify and respond to the shortcomings they present in light of advancements in MD simulations. This includes not only the refinement of current methods, but also the development of new visualization metaphors for MD simulations. These new representations should aim to simplify both temporal and spatial scale complexity to reduce the cognitive burden and facilitate the identification of significant events within the simulations. Furthermore, it is important to bridge the gap between the scientific visualization of raw simulation data and the analytical visualization. By linking the scientific visualization of the raw simulation data with the visualization of the analysis data, we hope to effectively navigate the complexity of molecular dynamics and open up new areas of understanding.

## Author contributions

HB: Data curation, Investigation, Visualization, Writing—original draft, Writing—review and editing. MS: Writing—review and editing, Conceptualization. MD: Writing—original draft, Writing—review and editing. MB: Writing—original draft, Writing—review and editing, Supervision and Validation. JJ-P: Writing—original draft, Writing—review and editing, Supervision and Validation.

## Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the "Initiative d'Excellence" program from the French State, grants "DYNAMO," ANR-11-LABX-0011, and "CACSICE," ANR-11-EQPX-0008. We thank ANR for support of grants "PIRATE," ANR-21-CE45-0014 and "MINOMICS," ANR-19-CE45-0017. MB thanks Sesame Ile-de-France for co-funding the display wall used for testing some of the visualization works discussed in the review.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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## References

Abraham, M., Apostolov, R., Barnoud, J., Bauer, P., Blau, C., Bonvin, A. M., et al. (2019). Sharing data from molecular simulations. *J. Chem. Inf. Model.* 59, 4093–4099. doi:10.1021/acs.jcim.9b00665

Alharbi, N., Krone, M., Chavent, M., and Laramee, R. S. (2018). "Vapli: novel visual abstraction for protein-lipid interactions," in 2018 IEEE Scientific Visualization Conference (SciVis), Berlin, Germany, 21-26 October 2018 (IEEE), 6–10.

Alharbi, N., Krone, M., Chavent, M., and Laramee, R. S. (2019a). *Hybrid visualization of protein-lipid and protein-protein interaction*. Genoa, Italy: VCBM, 213–223. doi:10.2312/vcbm.20191247

Alharbi, N., Krone, M., Chavent, M., and Laramee, R. S. (2019b). Lod pli: level of detail for visualizing time-dependent, protein-lipid interaction. *VISIGRAPP* 3, 164–174.

Alharbi, N., Laramee, R. S., and Chavent, M. (2016). Molpathfinder: interactive multi-dimensional path filtering of molecular dynamics simulation data. *CGVC*, 9–16. doi:10.2312/cgvc.20161289

Alharbi, R., Strnad, O., Luidolt, L. R., Waldner, M., Kouril, D., Bohak, C., et al. (2021). Nanotilus: generator of immersive guided-tours in crowded 3d environments. *IEEE Trans. Vis. Comput. Graph.* 29, 1860–1875. doi:10.1109/tvcg.2021.3133592

Allen, W. J., Lemkul, J. A., and Bevan, D. R. (2009). Gridmat-md: a grid-based membrane analysis tool for use with molecular dynamics. *J. Comput. Chem.* 30, 1952–1958. doi:10.1002/jcc.21172

Amaro, R. E., and Mulholland, A. J. (2020). A community letter regarding sharing biomolecular simulation data for covid-19. *J. Chem. Inf. Model.* 60, 2653–2656. doi:10.1021/acs.jcim.0c00319

Arbon, R. E., Jones, A. J., Bratholm, L. A., Mitchell, T., and Glowacki, D. R. (2018). Sonifying stochastic walks on biomolecular energy landscapes. arXiv preprint arXiv: 1803.05805.

Baaden, M. (2023). Interactive Molecular Dynamics Encyclopedia Chapter. doi:10.17605/OSF.IO/CJP4D

Ballweg, H., Bronowska, A. K., and Vickers, P. (2016). "Interactive sonification for structural biology and structure-based drug design," in *Interactive Sonification Workshop* (Bielefeld, Germany: CITEC, Bielefeld University).

Battimelli, G., Ciccotti, G., and Greco, P. (2020). "Computer meets theoretical physics," in *The new frontier of molecular simulation the Frontiers collection* (Cham: Springer International Publishing). doi:10.1007/978-3-030-39399-1

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Le Muzic, M., Parulek, J., Stavrum, A. K., and Viola, I. (2014). “Illustrative visualization of molecular reactions using omniscient intelligence and passive agents,” in *EuroVis proceedings of the 16th eurographics conference on visualization* (Goslar: DEU: Eurographics Association), 141–150. doi:10.1111/cgf.12370

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