Lab of the future may be close to reality

November 04, 2021 // By Rich Pell
Lab of the future may be close to reality
A new report from research and advisory service firm Lux Research finds that the momentum for digital technology in lab research is growing and brings an increasingly broad and powerful digital toolkit that includes AI, robotics, and IoT sensors.

According to new data, says the firm, the already growing momentum for digital technologies in lab research was further accelerated by the COVID-19 pandemic, causing teams to rapidly adopt digital tools and rethink their current processes. While innovation and R&D consist of many activities, the new report - " The Lab of the Future " - focuses on lab research and looks at where the key digital developments are occurring.

These digital solutions, says the firm, come at a time when industries that make heavy use of lab research, such as chemicals and pharmaceuticals, continue to face declining productivity on top of new challenges like rising costs, environmental factors, long development cycles, and information overload. While individual instruments and processes have benefited from digital tools like automation and analytics, lab research has changed less over the past decades than many would have predicted.

However, the report finds, with an increasingly broad and powerful digital toolkit that includes tools like artificial intelligence (AI), robotics, and Internet of Things (IoT) sensors, the lab of the future - one that is significantly more automated, efficient, and effective - may be closer to reality. After an initial wave of hype and activity in the late 1990s, innovation interest in applying digital tools to the lab plateaued for nearly a decade.

However, starting around 2013, there has been a steady growth in innovation interest, showing that the space may be in a phase where it could lead to a significant impact. While there are many digital use-cases and technologies available to enhance lab research, they fall into three broad categories:

  • Modeling and Informatics – Using modeling and informatics tools like machine learning to accelerate the development and discovery process.

    • Example: Using machine learning to model and predict polymer properties to shorten the overall polymer design time.
  • Knowledge Management – Systematically capturing, analyzing, and distributing knowledge throughout an R&D organization.
    • Example: Using natural language processing to

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