Revolutionizing Experimentation- The Latest Innovations in Experiment Process Automation

When it comes to scientific research and development, it is absolutely crucial to conduct experiments with the utmost efficiency and accuracy. As we dive even deeper into the era of digital technology and data science, the automation of these experimental workflows becomes both a necessity and an opportunity. Experiment Process Automation (EPA) provides an exciting framework for doing just that. By automating not only the manual aspects but also—whenever possible—the laborious, repetitive algorithms for data analysis, we furnish our enterprises with increased cycle times, better reproducibility, and ultimately, higher quality science.

The artificial intelligence and machine learning fields are transforming the way scientists automate experiments. Intelligence systems being developed now can learn from data, make predictions, and optimize the conditions of an experiment. Analyzing big data enables artificial intelligence algorithms to predict outcomes of potential experiments, greatly reducing the need for the traditional approach of trying many things until hitting on one that works. Plus, staying adaptive, artificial intelligence can adjust experiment protocols in real time for efficiency and effectiveness.

Laboratory operations are being transformed by Robotic Process Automation. RPA is an emerging technology that is being rapidly adopted in many industries and now in laboratories to automate repetitive, error-prone, and time-consuming tasks. RPA can handle many different aspects of the experimental process, from sample preparation to data collection and analysis. Indeed, the RPA software being employed in laboratories to automate pipetting and other precision-based tasks certainly seems well-suited, given the requirement for LIMS to be integrated with those activities.

IoT devices are getting integrated into labs at an increasing rate. These devices are changing laboratories into much smarter spaces. During my recent temp job at a big electron microscope "foundry" lab, but also in my big-data-for-biology office hours, I got to experience the integration of the internet into current laboratory practices. We can now know and achieve so much more! But there's still way too much room for improvement in how we integrate the internet into the current office-hours laboratory experience.

The way that experimental data is stored, processed, and analyzed is undergoing a fundamental change with the advent of cloud computing and advanced data analytics. During an era when the "architecture" of the drug molecule still dominates "good science," where one or two chemists in an academic lab solve structure and define the target–chemistry interface, the need for efficient use of chemical data and the increasing pace of drug discovery demand a totally different approach in order to see the interactions between drug candidates and the target. Platform pressure is driving the adoption of an entirely new informatics system that also completely changes how chemists interact with the data landscape.

Virtual reality (VR) and augmented reality (AR) are being used in ever-more creative ways to streamline the research process and amp up its productivity. Both VR and AR offer big boosts to scientific research in situations where the right kind of technology can make a huge difference—especially when it comes to the precarious act of "doing science." Innovative applications employing these technologies offer ways to cut time and cost from the various parts of the scientific process, and to do so in part simply by getting the right bits of information to the right people in the right way.

The trustworthiness and the path an experimental datum follows are of utmost importance. "Ensuring the integrity and traceability of experimental data are essential.," Eberhart writes. We might take it for granted that the results of an experiment can be trusted, but (thankfully) researchers are also human and can make mistakes and, rarely, can also be motivated to fudge the data. Collaboration among a diverse range of parties can also introduce opportunities for misconduct.

The scientific study and development process is changing dramatically through the advent of new technologies, most notably automated experimentation, that are rapidly and fundamentally altering the way scientists work. Researchers are now using an array of technologies to increase both the scale and the quality of their work. And they’re couple those tools and techniques with outsized expectations of the kinds of science they can do.

The clearest way to see this is in the use of artificial intelligence to automate laboratory work. AI has the potential to transform what has been a largely "artisanal" enterprise whereby scientists plan their own experiments and then, for the most part, carry them out by hand. AI is already leading to major changes in the way laboratories work and what kinds of science they can do.