# Virtual reality in drug design: Benefits, applications and industrial perspectives

Marc Baaden, David R Glowacki

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## To cite this version:

Marc Baaden, David R Glowacki. Virtual reality in drug design: Benefits, applications and industrial perspectives. Current Opinion in Structural Biology, 2025, 92, pp.103044. 10.1016/j.sbi.2025.103044. hal-05024222

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# Virtual reality in drug design: Benefits, applications and industrial perspectives

Marc Baaden¹ and David R. Glowacki²

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Virtual reality (VR) is a tool which has transformative potential in domains which involve the visualization of complex 3D data such as structure-based drug design (SBDD), where it offers new ways to visualize and manipulate complex molecular structures in three dimensions, and enable intuitive exploration of protein-ligand complexes. In this article, we outline three levels of interaction which are available in immersive VR environments for drug discovery, and provide illustrative case studies with applications in COVID-19 research and protein-ligand docking. We discuss VR's role in drug discovery based on conversations with experts from the pharmaceutical industry. While industry experts are mostly optimistic about the potential of VR, they point to the challenges related to integration with existing workflows and the need for improved hardware ergonomics, as well as ensuring a synergistic relationship between VR and an expanding suite of artificial intelligence (AI) tools.

## Addresses

¹ Université Paris Cité, CNRS, Laboratoire de Biochimie Théorique, 13 rue Pierre et Marie Curie, 75005, Paris, France

² Intangible Realities Laboratory, CiTIUS~Centro Singular de Investigación en Tecnoloxías Intelixentes da USC, Rúa de Jenaro de la Fuente Domínguez s/n, 15782, Santiago de Compostela, Spain

Corresponding authors: Baaden, Marc (baaden@smplinux.de); Glowacki, David R. (drglowacki@gmail.com)

**Current Opinion in Structural Biology** 2025, 92:103044

This review comes from a themed issue on **New Concepts in Drug Discovery (2025)**

Edited by Philip Biggin and Roderick Hubbard

For a complete overview see the Issue and the Editorial

Available online xxx

https://doi.org/10.1016/j.sbi.2025.103044

0959-440X/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

## Introduction

The process by which a small drug molecule interacts with a protein target can be described as a kind of shape-matching puzzle akin to Tetris [1], albeit considerably more complicated, owing to the fact that it occurs in 4 dimensions. Whereas Tetris is restricted to ‘flatland’ — i.e., it involves matching the shapes of 2D objects, molecules and proteins are 3D [2,3]. The fourth dimension arises from the fact that molecular shapes and

protein shapes are flexible and change with time. Structure-based drug design (SBDD) takes this additional complexity into account, forming an important part of modern drug discovery — i.e., understanding the detailed 3D structures of biomolecules plays a crucial role in the development of targeted therapies [4–6]. By using structural data obtained from techniques such as X-ray crystallography and cryo-electron microscopy, SBDD researchers can design molecules that interact precisely with biological targets, enhancing the likelihood of developing effective and specific drugs, enabling more rational and targeted development strategies.

The importance of visualization in understanding biomolecular structure can be traced back decades, to times when researchers relied on physical models and early computer graphics to study molecular and protein structures [7,8]. The advent of increasingly sophisticated computational chemistry tools that allow molecular models to be visualized on computer screens has improved researchers’ ability to assess binding sites, understand protein-ligand interactions and hypothesize modifications that could improve drug efficacy [5]. Nevertheless, both molecular biology and human perception take place in four dimensions, so even the most advanced visualization software on conventional 2D computer monitors collapses 4D complexity into low-dimensional representations, on which it can be challenging to fully perceive and interact with complex structures.

Virtual reality (VR) offers the possibility to natively visualize complex data (static or time-dependent) in 3 spatial dimensions, which previous studies have shown can be more efficient to navigate than a 2D representation [9,10]. As such, VR has applications within a range of industries which rely on the visualization of complex 3D environments, products, and data structures. The potential for molecular graphics [11,12], modeling [13,14], and drug design [15,16] was recognized early on by both academia and industry. Within high-tech industries spanning healthcare and surgical training, architecture and engineering, design & manufacturing, automotives, e-commerce, education and remote learning, aerospace, and digital animation, VR is being adopted as part of a larger wave of innovation tools — including for example artificial intelligence (AI), robotics, and big data [17,18]. For structural biology, a
vision of “conceptualizing a 4D virtual reality of cells using digital twins” has been suggested [19].

In the pharmaceutical industry, VR has the potential to make computer-aided drug design (CADD) more efficient, data-driven and interactive, overcoming some of the challenges faced by SBDD methods. The potential for medicinal chemistry [20] and drug design has been explored from various angles, such as biomolecular structure education [21], virtually practicing drug design [22], and taking advantage of the immersive interactivity [23]. A number of standalone software packages with specific VR use are described in the literature, including ChimeraX [24], Nanome [25], UnityMol [26], DockIT [27], VRPharmer [28] and VRChem [29]. There are some web-based implementations such as ProteinVR [30], VRmol [31] and VISPER [32]. Notable use cases reported in the literature concern the development of inhibitors during the SARS-CoV-2 pandemic [33], the exploration of the chemical space of Drugbank compounds [34] and the modeling of protein-ligand unbinding pathways [35,36]. One of the advantages of VR lies in the analysis and interpretation of complex 3D ligand-protein structures, as it often involves identifying subtle spatial relationships, interactions and conformational changes that are difficult to characterize even with automated industry-standard software tools [5]. In principle, more efficient and accurate spatial cognition has the potential to improve the accuracy of hypotheses related to molecular fit and binding affinity, as well as the decision-making process during drug optimization. Moreover, VR may improve collaboration [37,38] between multidisciplinary SBDD teams, allowing experts to simultaneously and interactively engage with the same 3D structural models in a shared, tangible format.

In what follows, we outline some of the ways in which VR can support CADD and SBDD, focusing on its potential to improve the efficiency and accuracy of the drug design process and pave the way for a more integrated, interactive and efficient workflow. We comment on how VR fits within the wider trend of technological innovation in drug discovery, particularly with respect to AI, and highlight challenges and limitations to VR adoption. The ideas presented herein have been informed in part by synthesizing feedback from 19 pharmaceutical industry workers with a diverse range of backgrounds (chemists, structural biologists, project managers, technologists, etc.) and career levels, all of whom we asked to comment on various issues related to the use of VR within the drug discovery process.

## Structure-based drug design with VR

### VR hardware & software for molecular simulation

The success of VR in structure-based drug design hinges on a seamless user experience that is inextricably linked to hardware capabilities. A detailed history and technical

outline of the various types of VR technology is beyond the scope of this short review, but has been discussed elsewhere [23,24,40,41]. We have focused our discussion on the recent generation of VR equipment [42–44], as illustrated in Figure 1. Broadly speaking, this latest generation of VR technologies (e.g., the Valve Index, the HTC Vive, the Meta Quest, the Pico Neo, and others) consist of a Head-Mounted Display (HMD) and 2 hand controllers, each of which has 6-Degree-of-Freedom (DoF) tracking of both rotational and translational motion in the x, y, and z directions. 6-DoF tracking for the HMD and the handsets enables the user to explore their full range of motion in order to change their perspective to explore the simulated environment. The latest VR headsets are implementing increasingly robust hand-tracking capabilities [45], which can be used in place of (or alongside) the hand-held controllers, enabling researchers to manipulate simulated molecular models as if they were physical objects, as illustrated in Figure 1 — eg, researchers can use natural gestures along with fine motor control to rotate & manipulate the structure in various ways. They can move their head to inspect structures from a different angle in order to gain a better understanding of its detailed 3D structure, just as they would with a physical model. However, the interactive visualizations available in VR are more powerful than those available with physical molecular models, given that they provide access to options for interacting with various aspects of the molecular representation that have become commonplace in 2D molecular visualization software. Distinct from 2D interfaces, VR with 6-DoF tracking creates a sense of spatial presence and embodiment, allowing users to feel as though they are interacting directly with 3D objects. This sensation not only enhances spatial comprehension but enables novel approaches to tasks such as protein-ligand docking [35,39,46–48] and the interactive manipulation of real-time molecular dynamics simulations [10], where users can literally ‘reach out and touch’ molecular simulations. Conventionally, the sense of ‘touch’ in VR is usually associated with haptic technologies; however, other sensory modalities including proprioception can also give a sensation of ‘touch’ even in the absence of haptic hardware, as outlined in detail by Glowacki and coworkers [40,49]. Figure 1 illustrates another unique possibility for VR: namely the ability to support collaboration between multiple researchers (who can either be located in the same physical space, or distributed remotely across different physical spaces) each of whom can inhabit the virtual space and interact with the molecule as if it were a tangible object, deriving similar benefits from the enhanced 3D spatial perception which VR software enables [40,50,51]. The sensation of presence, in particular in collaborative settings, can be strengthened through features such as eye gaze tracking and visualization [52,53]. Finally, we note that the vast majority of VR HMDs enable VR applications to be
<figure><img src="image_4.png" /><figcaption>**Figure 1**</figcaption></figure>

**Protein-ligand rebinding using interactive molecular dynamics in virtual reality (iMD-VR).** The figure shows a schematic of two scientists wearing VR Head-Mounted Displays (HMDs) and using handheld wireless controllers along with an open-source multiperson iMD-VR framework [39] to manipulate a real-time MD simulation of oseltamivir binding to the neuraminidase protein. This image is used under CC-BY License from Ref. [39].

easily extended to Augmented Reality (AR) using so called ‘video passthrough’ functionality, which allows the user to see the world through cameras which are integrated into the headset. It is important for readers to understand that we intend our use of the term ‘VR’ to include HMD-integrated AR functionality.

## Defining “interactivity”

The interaction between researcher and molecular structure in VR can be systematically divided into three basic tasks: Examine, Explore, and Manipulate [54]. These tasks correspond to three different levels of “interactivity” within VR molecular visualization, as illustrated in Figure 2.

At the examination (or visual analysis) level, researchers can explore structures by using controllers or hand tracking to move, rotate or scale the visualization as well as adjust the perspective of their HMD to view the molecule from a different angle. Beyond merely viewing the molecular object, certain properties and analyses can enrich the visual inspection in the “Examine” (or visual analysis) task. At the exploration level, researchers can alter the visualization itself by switching between representations such as ball-and-stick, ribbon or electrostatic surface views to highlight different structural features relevant to understanding the target system or watching a motion time series from a molecular dynamics simulation to explore in-depth selected features of interest. Finally, at the manipulation level, researchers can actively modify the structure or influence the

underlying simulation — for example, by adjusting the structure of a drug molecule to improve binding properties, or by controlling a molecular simulation in real time (as in interactive molecular dynamics in virtual reality, iMD-VR [10,23,35,36,39,40,46,47,50]), which allows a user to manipulate the motion of individual atoms and molecules by integrating a user defined force into the equations of motion for a real-time MD simulation, using the mathematics described by O'Connor et al. [40]. To date, iMD-VR represents perhaps the best documented example of VR applied to molecular science within the available research literature. For example, a quick search for “iMD-VR” on Google Scholar at the time of writing this article shows more than 20 publications over the past 7 years discussing various aspects of iMD-VR applied to molecular simulation. Moreover, studies carried out using iMD-VR are amongst the most rigorous VR studies available in the literature, offering an answer to a question that frequently arises: how does VR compare to keyboard/mouse interfaces in molecular science applications? In fact, there are relatively few studies that directly address this question. The only study we know which directly quantified the benefits of VR vs. keyboard/mouse vs. touchscreen is the 2018 study by O'Connor et al. [10], which specifically examined how quickly users could accomplish representative molecular spatial manipulation tasks. O'Connor et al. showed that for tasks which involve complex 3d manipulation, iMD-VR offered accelerations of 2x - 10x. The vast majority of VR applications for molecular science have not been the target of
<figure><img src="image_5.png" /><figcaption>**Figure 2**</figcaption></figure>

**Core interactive tasks in virtual reality-enabled drug design.** The figure illustrates three fundamental categories of VR-assisted activities: (a) Visual analysis - immersive visualization and inspection of a protein cofactor binding site; (b) Exploration - strategic placement of pharmacophoric features using UnityMol-Pharmit, guided by the identification of empty space in a binding pocket; and (c) Manipulation - sequential stages of interactive fragment docking, where an aromatic moiety is positioned according to π-stacking interaction fields shown as green isosurfaces. Central panel courtesy of Florent Barbault. The Image is under CC-BY License by Marc Baaden.

focused human-computer-interaction (HCI) research studies, making it difficult to directly compare to keyboard/mouse technologies. Moving forward, it would be valuable to extend HCI study designs to other software frameworks and molecular applications. Table 1 provides a list of some VR software currently available for drug design, along with some description of their level of interactivity.

## VR use-case examples

Visualizing molecular properties such as hydrogen bonds and electrostatic potential in VR can aid drug design. Electrostatic interactions, including hydrogen bonds and salt bridges, are essential for protein stability and ligand binding [60]. VR programs such as UnityMol [26,61], Molecular Rift [45], Nanome [25], and Peppy [62] allow users to toggle hydrogen bond visualization, making interactions easier to examine with depth perception, which can help identify modifications to improve binding – e.g., enhancing H-bond visibility by using 3D representations beyond 2D dotted lines. A molecule's overall electrostatic potential can help to predict binding sites. UnityMol-APBS [26] integrates VR with adaptive Poisson Boltzmann solver software [63], enabling visualization of electrostatic potentials. Users can prepare input files and execute calculations within the VR environment, simplifying the process with intuitive gestures, reducing the need for extensive prior knowledge.

Testing UnityMol-APBS on the enzyme *Torpedo californica* acetylcholinesterase highlights several of VR's benefits: the large field of view and animated electrostatic field lines made the active site easily identifiable, enabling users to move and explore the substrate within the binding cavity, providing a tangible sense of binding mode feasibility. In another example [30], the developers of ProteinVR tested visualization of a protein-ligand complex (*T. brucei* RNA editing ligase 1 complex) in VR versus visualization using the 2D molecular viewer VMD. Using a modeled structure obtained with AutoDock Vina, they were able to easily explore the deep, narrow binding pocket of the complex. This approach allowed users to hypothesize that the docking pose might be incorrect and suggest better interactions. This example highlights how VR's immersive nature enhances spatial awareness, revealing structural details that are difficult to perceive on 2D screens.

The use of VR played an important role during the recent COVID epidemic, helping researchers to quickly visualize and understand the biomolecular structures that were associated with SARS-CoV-2 infection and boost the potential for new therapies [64]. For example, expert iMD-VR users were able to generate structures of a small molecule inhibitor and an 11-amino acid oligopeptide substrate complexed with the SARS-CoV-2 main protease (Mpro), crucial for viral replication. The
<table><caption>**Table 1**</caption><caption>Currently established VR software platforms that support SBDD with a wide range of features. The table lists key characteristics in terms of commercial availability status, source code accessibility, multi-user collaboration capability, support for interactive molecular dynamics simulations, primary references, and a brief summary of each platform's unique strengths or outstanding capabilities. Only actively maintained software with extensive functionality and associated with drug design applications was considered. All tools allow to manipulate the ligand and receptor independently.</caption><thead><tr><th>Name</th><th>Availability</th><th>Source Code</th><th>Collaborate</th><th>iMD</th><th>Lig/Rec</th><th>Ref.</th><th>Highlights</th></tr></thead><tbody><tr><td>ChimeraX</td><td>Academic</td><td>Open</td><td>Multi-user</td><td>Repacking</td><td>Yes</td><td>[55]</td><td>Versatile, oriented towards structural biology: Extensive structure exploration, atomic refinement, cryoEM and density map support</td></tr><tr><td>Nanome</td><td>Commercial</td><td>Closed</td><td>Multi-user</td><td>—</td><td>Yes</td><td>[56]</td><td>Specific SBDD orientation: Property calculations, docking, mutagenesis, molecular dynamics support. Advanced avatars.</td></tr><tr><td>Nanover (formerly Narupa)</td><td>Academic</td><td>Open</td><td>Multi-user</td><td>Native</td><td>Yes</td><td>[57]</td><td>Focus on interactive manipulation, including SBDD: Advanced interactive and collaborative molecular dynamics, python-based data analysis (Jupyter notebooks)</td></tr><tr><td>Samson</td><td>Commercial</td><td>Closed</td><td>—</td><td>—</td><td>Yes</td><td>[58]</td><td>Versatile, with VR support: Broad modeling and visualization tools including reactive force fields and interactive MD</td></tr><tr><td>UnityMol</td><td>Academic</td><td>Open</td><td>Multi-user</td><td>via IMD</td><td>Yes</td><td>[48]</td><td>Focus on visual exploration, interactive simulations and adaptability: Electrostatics, field lines, multi-scale simulations</td></tr><tr><td>Yasara</td><td>Commercial</td><td>Closed</td><td>—</td><td>Native</td><td>Yes</td><td>[59]</td><td>Versatile with highly optimized performance: Seamless desktop to VR transition, fluid display of multi-million-atom scenes</td></tr></tbody></table>

experts successfully replicated experimentally observed crystal structures and highlighted the importance of forming hydrogen bonds for stability. They used iMD-VR to interactively dock an inhibitor and substrate to the apo form of Mpro, yielding stable MD structures. A comparison of peptide-Mpro complexes docked using iMD-VR, AutoDock CrankPep, and PyMOL showed strong agreement, demonstrating reliability across methods [46,47]. The COVID-19 pandemic spurred the EXSCALATE4CoV consortium, led by Dompé farmaceutici S.p.A., involving 18 organizations to enhance pandemic response. Partnering with the Nanome company, the team conducted homology modeling, molecular dynamics, and virtual screening to study SARS-CoV-2 and identify drug targets. Interacting in the VR environment, scientists were able to better understand the virus's molecular dynamics and key chemical interactions [33], and generate structural analogues of a small molecule with the aim of creating a SARS-CoV-2 main protease (Mpro) inhibitor [65].

Kingsley et al. [25] highlight an example where Nanome users constructed a small molecule inhibitor in the active site of RIP2 kinase and achieved an RMSD of 1.8 Å compared to the crystal structure, within the typical 2 Å cutoff for docking accuracy. Starting from the crystal structure, users made chemical modifications to form additional hydrogen bonds for a tighter binding pose, demonstrating the ability to create drug-protein structures similar to crystal structures and predict

improved interactions. In another example, chemists were able to discuss potential macrocyclization sites that would enhance physicochemical properties while retaining activity [25]. When an appropriate target structure is unknown, VR may assist in integrative modeling of the drug targets themselves as well as their functional states [66].

While automated docking is widely used in CADD for predicting the binding poses and affinities of small molecules to target receptors, docking flexible molecules remains challenging and often requires human input. Deeks et al. explored the use of iMD-VR for human-guided, flexible protein-ligand docking [39]. They found that expert users alike could guide ligands (benzamidine, oseltamivir, and amprenavir) into the binding pockets of viral enzymes (trypsin, neuraminidase, and HIV-1 protease) and recreate stable crystallographic binding poses within 2.15 Å RMSD in 5–10 min. Non-experts were able to produce accurate docked structures after just 40 min, including training time.

## Industrial perspectives: collaboration & AI integration

Broadly speaking, as shown in Box 1, pharmaceutical industry respondents with whom we spoke as we were preparing this article seemed cautiously optimistic about VR. While VR offers benefits in terms of
**Box 1. Summary of the views on VR for drug discovery obtained from emails & discussions with 19 industry experts.**

1. The Role of Visualization in Drug Discovery Pipelines

**Consensus on Importance:** Most agreed that visualization is essential in drug discovery, particularly in understanding molecular structures, examining protein-ligand interactions, and facilitating communication among teams. Visualization supports hypothesis formation, SAR (structure-activity relationships) analysis, and design-cycle decision-making.

**Interactive Visualization as a Necessity:** Several experts highlighted that while 2D visualization is valuable, 3D visualization offers richer insights, especially for non-specialists, such as medicinal chemists, who may not have an inherent understanding of molecular models. Effective visualization thus aids collaborative and interdisciplinary workflows, supporting a clearer, shared understanding of structural data.

2. Degree of VR Adoption in the Industry

**Low to Moderate Adoption:** Strong consensus that VR adoption is currently low in the pharmaceutical industry. Despite some excitement, most companies have not implemented it on a large scale. When used, VR is generally limited to specific applications or pilot initiatives. Experts attributed this to a combination of technological, logistical, and organizational factors.

**Pockets of VR Use in Larger Companies:** Some large companies, like Novartis and Genentech, have experimented with VR (e.g., Nanome) to facilitate collaborative visualization. However, these are often isolated cases rather than widespread practices, and VR is generally not regarded as a critical tool for daily operations.

3. Perception of VR Among Drug Designers

**Positive yet Cautious:** While many experts view VR positively, seeing its potential to enhance molecular visualization and foster collaboration, there remains a cautious outlook regarding its role as a day-to-day tool. Many drug designers see VR as an engaging and 'cool' tool, but they are unclear whether it is essential or transformative for enhancing their core tasks.

**Considered a "Nice-to-Have":** Some experts noted that VR could add value to visualization, but it remains supplementary rather than fundamental. VR is often seen as helpful for educational purposes, visual storytelling, or presentations to stakeholders, yet for drug design itself, VR does not yet replace existing tools.

**Perception of VR as a "Gimmick":** A few experts held a minority view that VR offers little beyond traditional visualization tools, describing it as a "gimmick".

4. Impact of AI on the relevance of VR

**Complementary Technologies:** Many experts noted that AI and VR are complementary rather than competitive. AI can enhance VR by enabling real-time data analysis, predictions, and suggestions within the VR environment, thereby making VR more responsive and informative. AI-powered data integration could enable more intuitive visualization of complex datasets.

**Increased Data Complexity Driving VR Need:** Some experts suggested that AI, by generating vast and complex datasets, increases the need for advanced visualization such as VR. As AI models become central to drug discovery, VR could help make complex data outputs more accessible and understandable.

**Higher Investment in AI over VR:** Despite the potential AI-VR synergy, several experts observed that AI now receives more funding and attention in industry than VR, as AI directly addresses predictive modeling and generative tasks. Given limited resources, many organizations prioritize AI over VR.

5. Challenges and Barriers to VR Adoption

**Cost and Resource Investment:** High initial costs of VR hardware, software, and maintenance emerged as a common barrier. Several experts noted that the ROI of VR investments remains unclear, especially since traditional visualization tools already fulfill many industry needs.

**Compatibility with IT Systems:** VR headsets and software often face security challenges, particularly with standalone devices that require extensive IT support to comply with industry standards. This adds administrative complexity and raises concerns about data security, a critical issue in handling sensitive pharmaceutical information.
**Complexity and User Training:** VR's complexity was noted as a deterrent. Many experts mentioned that VR tools require significant user training, which is difficult to justify given the steep learning curve and limited perceived benefit. Furthermore, VR's unique handsets and headset controls complicate usage and deter potential users accustomed to simpler tools.

**Physical Discomfort and Social Perception:** Bulky headsets and the discomfort associated with prolonged VR use were frequently cited as challenges. VR headsets are often perceived as isolating or "geeky," which affects their acceptance in collaborative settings. Some experts suggested that augmented reality (AR) might be preferable.

visualization, collaboration, and interaction, it faces significant barriers related to cost, usability, and integration. Most experts agree that VR is promising but requires further development and industry-specific tailoring to become essential to drug discovery workflows. One expert identified some clear examples where VR helped medicinal chemists to propose more creative structures than would have been possible using 2D interfaces, remarking on how VR's biggest impact arises from the ability to visualize binding pockets in 3D. One of VR's greatest values may lie in enabling collaboration, allowing remote users to interact in the same virtual environment, regardless of physical location, particularly in global companies with distributed teams, as occurred during the COVID epidemic. Another expert highlighted VR's important role in facilitating collaboration between multidisciplinary teams of the sort often involved in small molecule SBDD projects, where regular meetings often involve medicinal chemists, modelers, and structural biologists. During these meetings, structural biologists share structural data, modelers present design ideas, and chemists challenge models and propose further iterations. Traditional 2D tools are often insufficient for supporting dynamic, real-time discussions amongst these teams; however, advanced visualization tools such as VR can help ensure that all the team members fully understand the structural hypotheses and interactions, facilitating shared understanding and real-time problem-solving, enhanced communication, and better informed decisions during the drug discovery process.

For VR to become more widely adopted, the tools need to integrate better with existing workflows, with dedicated training and support for those unfamiliar with VR. In this way, VR will become more intuitive and capable of providing clear productivity benefits. Experts emphasized that VR tools need to integrate smoothly with current drug discovery tools and data infrastructures, connecting effortlessly to databases, electronic lab notebooks, and existing molecular modeling platforms to avoid workflow disruptions. Simplifying VR controls, reducing the bulkiness of headsets, ensuring compatibility with existing hardware, and ensuring industry-level security would lower adoption barriers and make VR more accessible for daily use.

AI-enhanced VR tools and collaborative features could make VR more attractive to industry users, helping VR

evolve from a niche tool to a mainstream component of drug discovery. Several experts highlighted that VR and AI serve complementary functions within the drug discovery process. AI is primarily seen as a powerful tool for data analysis, property prediction, and generative chemistry, while VR provides an immersive platform to visualize and interact with complex molecular data. Experts noted that integrating AI capabilities into VR could make VR more relevant and user-friendly, e.g., to offer predictive modeling, generate interactive feedback, and simplify complex user interactions. AI-driven features such as voice commands or automated adjustments could streamline VR experiences, allowing real-time data analysis, intelligent assistance, and automated tasks. With the surge of large datasets in drug discovery, the combined use of AI and VR can facilitate better data interpretation. Experts suggested VR could offer a powerful interface for researchers to navigate the large datasets produced by AI. This approach could improve the analysis of 3D protein structures and binding interactions.

Despite VR's potential, some experts suggested that AI's prominence in drug discovery has overshadowed VR. At present, AI tends to secure more funding, attention, and adoption due to its immediate impact on property predictions and automated workflows. This focus has limited VR's growth and refinement. Some experts expressed that VR may struggle to gain significant traction without better integration with AI. While many respondents highlighted AI's potential to make VR more relevant and powerful, some believed that VR and AI address different challenges in drug discovery: AI handles complex computations and data generation, while VR focuses more on visualization and user interaction, suggesting that their convergence is beneficial but not essential. Experts cited practical challenges in merging VR with AI, including the complexity of developing integrated software and the need for significant user training. The rapid evolution of both technologies poses difficulties for organizations in adopting and maintaining these systems. Additionally, AI-enhanced VR tools require robust hardware and software, which can increase costs and complicate IT infrastructure.

## Future perspectives & conclusions

VR holds significant unexplored potential to mark a paradigm shift in drug development, beyond its current limited applications. The transition from traditional 3D
molecule viewers to fully immersive VR environments represents more than just an incremental improvement — it is a fundamental shift in the way scientists experience and manipulate molecular structures. With VR, drug developers can now enter a ligand-protein complex and develop an intuitive spatial understanding that mirrors the way we explore and interact with the physical world. The knowledge gained about 3D structures is important in most phases of drug discovery. As long as humans are involved in the drug design process, VR offers an excellent tool to make faster and better decisions. In addition, VR enriches the intuitive aspects of drug design and catalyzes creative breakthroughs in development. Therefore, VR has potential in the pharmaceutical industry, especially as a visualization, thinking and collaboration tool, but currently remains secondary to AI and traditional visualization platforms. Future advances in VR hardware, software usability and integration with AI-powered analytics could be key to unlocking the full potential of VR in drug discovery. The barrier between virtual and augmented reality is likely to weaken, which will facilitate the adoption of such technologies. Further research will be required to determine whether immersive molecular science applications using HMDs are best suited to immersive VR or immersive AR. To date, neither VR nor AR appear to be very well integrated into existing pharmaceutical research workflows, in part because such workflows are extremely bespoke, making it complicated to develop a ‘one-size-fits-all’ solution. It is our hope that this article may help to motivate such work, in order to make VR a more useful tool in the drug design process. And last but not least, VR could play an essential role in the efficient training of future generations of drug developers [36,67].

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We gratefully acknowledge the insights and contributions of numerous colleagues in the field of VR-assisted structure-based drug design, in particular the nineteen industry professionals who participated in our sentiment analysis survey. MB is supported by grants from the French government program "Initiative d'Excellence" (DYNAMO: ANR-11-LABX-0011; CACSICE: ANR-11-EQPX-0008) and the Agence Nationale de la Recherche (PIRATE: ANR-21-CE45-0014). DRG acknowledges support from the European Research Council under the European Union's Horizon 2020 research and innovation programme through consolidator Grant NANOVR 866559, the Axencia Galega de Innovación for funding as an Investigador Distinguido through the

Oportunius Program, and the Xunta de Galicia (Centro de investigación de Galicia accreditation 2019–2022, ED431G-2019/04).

## Data availability

No data was used for the research described in the article.

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