Artificial intelligence and robotics are changing how pharmaceutical companies conduct research and development. While public attention often centers on faster drug discovery and personalized treatments, some of the most consequential shifts are happening inside labs.
Major firms are now using automated systems not just to accelerate R&D, but to rethink how drugs are designed, tested, and produced. These developments hold clear scientific potential but also raise questions about transparency, access, and the direction of innovation in healthcare.
Image Credit: Gorodenkoff/Shutterstock.com
Download your PDF copy now!
Inside the Automated Lab
Manual pipetting and trial-and-error molecule screening are no longer the norm in early-stage drug development. Instead, much of today’s pharmaceutical research happens in automated, “lights-out” labs—environments where robotic systems carry out experiments continuously, with minimal human oversight.1,2
These systems handle everything from automated synthesis of novel chemical compounds to high-throughput screening of thousands of molecules against biological targets simultaneously, tasks that would take human scientists months to complete in days or hours. Cloud robotics platforms enable remote control and data sharing across global research centers, while AI algorithms analyze the resulting torrent of data, predicting compound efficacy, toxicity, and optimal formulation with growing accuracy.3,4
Companies like Insilico Medicine and Exscientia pioneered this approach, using AI to design drug candidates in silico (via computer simulation) before any physical synthesis occurs. Their platforms, and those now adopted by major Pharma, utilize deep learning models trained on massive datasets encompassing chemical structures, genomic information, proteomics, and historical clinical trial results.2,5
Technologies like AlphaFold, well-known for accurately predicting protein structures, highlight the power of AI in uncovering drug targets that were once considered out of reach. Robots take it from there, executing the AI’s instructions by precisely dispensing reagents, growing cells, running assays, and analyzing results.
Together, they form a closed-loop system where the AI continuously learns from each robotic experiment, refining its selection of promising candidates over time. This approach significantly shortens the drug discovery timeline and helps cut down the average $2.8 billion cost of bringing a new drug to market.1,2,4
The Strategic Silence Behind Automation
While Big Pharma is quick to highlight the advantages of automation—greater speed, lower costs, improved outcomes, it is less forthcoming about the underlying technologies. That’s by design. Competitive pressure and the value of proprietary tools make secrecy standard practice.
At its core, it's driven by intense competition and the immense value of intellectual property (IP). The algorithms powering their AI drug design tools, the unique architectures of their robotic workflows, and the vast, curated datasets they've amassed constitute fiercely guarded trade secrets, seen as critical moats protecting future revenue streams.4,6
Furthermore, the "black box" nature of complex AI models, particularly deep learning, often serves as a barrier to transparency. Companies frequently cite the difficulty in explaining how their AI systems arrive at specific drug candidates or predictions as a reason for withholding information. This lack of explainability is not merely a technical issue; it can also serve as a strategic advantage, allowing firms to keep their methodologies private.
In the race to be first to market, especially in lucrative therapeutic areas like oncology or immunology, companies tend to prioritize quick internal development and patent filing over disclosing methodologies or sharing data, resulting in significant innovations remaining hidden from public view and scientific scrutiny.4,6
What Gets Lost in the Black Box
The industry’s tight hold on its methods and data has significant consequences. One of the most immediate is the issue of scientific reproducibility. When researchers outside a company can’t access the models or datasets used to develop a drug, they can’t replicate the results. This makes it harder to assess what’s working, what isn’t, and why—a foundational problem for science.6
There are also concerns around bias and safety. AI systems trained on narrow or skewed datasets may inadvertently encode those biases into the drug development process. If the data used primarily reflects certain populations or over-relies on animal models, the resulting therapies may underperform—or carry risks—for patient groups not well represented in the training data. Without visibility into how these models are built and validated, it becomes difficult to audit or correct for those imbalances.4
This lack of access also creates innovation bottlenecks. Academic labs and smaller biotech companies often lack the resources to build large proprietary datasets or advanced automation infrastructure. As a result, much of the momentum in AI-driven drug development is concentrated in the hands of a few large firms, limiting collaboration and slowing the pace of broader scientific progress.6
Regulatory oversight adds another layer of complexity. Agencies like the Food and Drug Administration (FDA) and European Medicines Agency (EMA) are working to adapt their frameworks to evaluate drugs developed through AI systems, but opaque tools complicate the process. Without a clear view into how decisions are made within these systems, regulators face real challenges in assessing whether a drug is safe and effective for use.6
What It Means for Robotics Providers
For robotics firms working in healthcare and life sciences, these shifts present both opportunity and complexity.
The need for advanced lab automation—ranging from robotic arms for liquid handling to integrated systems for sample preparation and analysis—is growing rapidly. The pharmaceutical robotics market is projected to increase from $459 million in 2024 to $1.68 billion by 2034, reflecting the urgency for robotics providers to align closely with the sector’s evolving requirements.1
Pharmaceutical companies are no longer looking just for hardware. They need fully integrated systems where robotics and AI function as a cohesive unit. Providers that can deliver seamless interoperability between robotic platforms and advanced data analysis tools, particularly those that integrate smoothly with existing lab information management systems (LIMS) and electronic lab notebooks (ELN), are likely to have a competitive edge. Integration is no longer optional; it's an expectation.1,7
Flexibility is another key consideration. Drug discovery protocols vary widely, and robotic systems must be able to adapt without requiring major reengineering. Platforms that offer modular components, allowing labs to reconfigure systems quickly and efficiently, are especially well-suited to meet this demand for agility.6,7
However, the current lack of standardization across AI-robotics interfaces presents a serious challenge. Closed architectures and proprietary data formats hinder interoperability, making it difficult even for teams within the same organization to share information across tools. Robotics providers could play a role in solving this by working with pharmaceutical companies and standards organizations, such as the Institute of Electrical and Electronics Engineers (IEEE), to establish open protocols for communication and data formats that promote transparency and cross-compatibility.6,7
While much of the AI powering drug discovery may remain proprietary, robotics providers can still support transparency and reproducibility. By ensuring their systems generate comprehensive, auditable logs of all experimental steps—including details about reagents, conditions, and outcomes—they can help improve traceability and bolster confidence in research outcomes. These records are essential not only for regulatory compliance but also for internal validation and scientific review.6
The Path Forward: Balancing Intellectual Property with Scientific Responsibility
The use of AI and robotics in pharmaceutical R&D is advancing quickly, but the surrounding norms and infrastructure have yet to catch up. While it’s reasonable for companies to protect their competitive edge, the broader implications of closed systems—on trust, equity, and progress—are increasingly difficult to ignore.
A more open model won’t happen overnight, but there are practical steps forward. Regulators can require more transparency in safety-critical contexts. Journals and professional societies can establish clearer standards for AI-related disclosures. And companies can share methods and tools that aren’t core IP, or contribute to open datasets that benefit the field at large.
Want to Explore this Space Further?
Why not read the following articles:
Download your PDF copy now!
References and Further Reading
- The Rise of Pharma Robots: Transforming Drug Manufacturing and Research. (2025). RoboticsTomorrow. https://www.roboticstomorrow.com/news/2025/02/07/the-rise-of-pharma-robots-transforming-drug-manufacturing-and-research/24079/
- Malesu, V. K. (2025). Why Drug Discovery Needs Robots and Artificial Intelligence. News-Medical. https://www.news-medical.net/health/Why-Drug-Discovery-Needs-Robots-and-Artificial-Intelligence.aspx
- Vora, L. K. et al. (2023). Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics, 15(7), 1916. DOI:10.3390/pharmaceutics15071916. https://www.mdpi.com/1999-4923/15/7/1916
- Kanakia, A., Sale, M., Zhao, L., & Zhou, Z. (2025). AI In Action: Redefining Drug Discovery and Development. Clinical and Translational Science, 18(2), e70149. DOI:10.1111/cts.70149. https://ascpt.onlinelibrary.wiley.com/doi/10.1111/cts.70149
- Paul, D. et al. (2020). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80-93. DOI:10.1016/j.drudis.2020.10.010. https://www.sciencedirect.com/science/article/pii/S1359644620304256
- Fehr, J. et al. (2024). A trustworthy AI reality-check: The lack of transparency of artificial intelligence products in healthcare. Frontiers in Digital Health, 6, 1267290. DOI:10.3389/fdgth.2024.1267290. https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2024.1267290/full
- Karam, P. (2024). Quanser’s Roadmap to Starting a Robotics Lab. Quanser. https://www.quanser.com/blog/autonomous-systems/roadmap-to-starting-a-robotics-lab/
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.