Artificial intelligence is catalyzing major transformations in laboratory spaces.
The growing demand for dry labs is being fueled by advancements in mathematical modeling, computational data analysis, and generative design. Unlike wet laboratories, which rely on liquids, chemicals, and biological samples, dry labs are centered on computational methods and data-driven research.
Despite the continued importance of wet labs and hands-on experimentation, a leading global consulting firm projects that generative AI could contribute up to $28 billion annually to drug discovery, emphasizing the growing role of computational methods in scientific research.
Richard Cairnes, JLL’s PDS UK and EMEA Head of Life Sciences, observes a growing demand for dry labs as major pharmaceutical companies seek to modernize their infrastructure. This may involve establishing research facilities in new countries, enabling scientists to collaborate through cloud-based platforms, or adapting existing laboratories to ensure future-readiness and enhance current research capabilities.
Reflecting the evolving landscape of scientific research, institutions like the UK’s Wellcome Genome Campus are modernizing their infrastructure. This includes creating expansive open-plan areas specifically designed for dry lab work and data analysis, alongside dedicated spaces for high-performance computing and AI research. Similarly, Germany’s Max Delbrück Center for Molecular Medicine (MDC) in Berlin has significantly expanded its facilities to accommodate the growing importance of bioinformatics and computational biology in modern biomedical research.
Gul Dusi, JLL’s Managing Director for PDS Life Science Projects in the U.S., predicts that the more extensive use of modeling and AI will fundamentally reshape laboratory design, even as human scientists continue to play a pivotal role in scientific breakthroughs.
She explains that this shift influences the overall laboratory layout, necessitating adjustments to the number of benches, power and data connections, and ultimately impacting how people move and interact within the research space.
Dry doesn’t equate to basic
Dry labs don’t have the same infrastructure requirements as wet labs, yet they still require careful planning, including the installation of strong power and HVAC systems to support more advanced equipment.
The concept suggests that while dry labs could be established in repurposed buildings, some structures may not lend themselves well to adaptive reuse.
Dusi cites quantum computing labs as a key example.
She emphasizes the need to create an almost astronautical environment with no atmospheric pressure within the building, achieved through the use of tanks of nitrogen and argon gas, making it one of the most complex construction projects.
While power consumption is a key factor, Dusi points out that dry labs present other significant challenges. These include the need for robust structural support to handle heavy equipment, adherence to specific ceiling height restrictions, and the mitigation of potential vibration issues.
Cairnes concurs, highlighting that the specialized technical needs of dry lab tenants make speculative fit-outs challenging. The high capital outlay for diverse infrastructure increases the risk for developers and landlords, as these investments may not align with the specific requirements of future occupants.
He explained that while the construction costs of the physical building might not differ significantly from traditional laboratories, the incorporation of more complex AI, automation, and robotics equipment would inevitably increase the overall expenses. He further emphasized the importance of providing flexible laboratory spaces that effectively cater to the specific scientific needs and research plans of individual end-user scientists, a crucial aspect in the evolution of future laboratories.
Embracing digital tools boosts innovation speed
To achieve greater time and quality efficiencies, project management professionals are adopting digital tools and AI, making the construction of life sciences projects more strategic and cost-effective.
AI’s capacity to gather and interpret extensive data to extract meaningful insights can facilitate a variety of functions, including procurement planning, program scheduling, site safety management, and improving sustainability practices.
According to Cairnes, building information modeling (BIM) plays a crucial role in creating digital twins, which offer significant advantages in visualization and planning. One key benefit of BIM is its ability to detect potential clashes between building systems and structural elements, such as pipes, ductwork, and electricals with beams. This proactive identification helps prevent costly delays and rework during the construction process.
Dusi is most excited by the potential of AI to significantly improve the work experience and well-being of life sciences laboratory personnel. She believes that AI can play a crucial role in simulating various scenarios and generating evidence-based design solutions that enhance productivity and efficiency within these environments.
Dusi emphasizes that by considering factors such as scientists’ movement patterns, equipment accessibility, collaborative interactions, and environmental conditions like air quality and daylight, it is possible to design and build human-centered laboratories that enhance the research experience and accelerate scientific discoveries.
