The Missing Link in Lab Efficiency: Why Aren't Scientists Automating?
Intro
In my decade of experience, mostly in early discovery research, I've been part of the small minority using robots in the lab. Not just using, but also scripting, maintaining, validating, and repairing. As an automation engineer, I know how to translate wet lab protocols into higher throughput, automated ones. But why aren't more scientists using automation in their experiments? I’d like to give a brief overview of some advantages of lab automation, explore why more scientists aren’t utilizing this technology, and try to convince you why you should automate.
Advantages:
Automated workflows offer many advantages that can significantly improve the efficiency and accuracy of experiments. Here are a few key benefits:
Precision: Sample tracking and instrument process data ensure that the right sample ends up in the right spot. No more worries about dispensing errors or mix-ups.
High throughput: Liquid handlers can dispense liquid faster than any human can and at smaller volumes than any manual instrument. Reaction volumes are getting even smaller, surpassing the 1536 well-format as seen in droplet microfluidics.
Consistency: Proper instrument maintenance ensures that dispensing pressure remains constant, resulting in more even dispense for cells, more correlation between replicates(and operators), and lower CV.
This is why not:
Even with the tools available, many scientists still rely on manual protocols. This could be due to several factors, such as:
Lack of awareness of the benefits of automation.
Lack of support from management because of high upfront costs.
Limited training and support for scientists in using automation systems.
This is why you should think again:
The advent of AI drug discovery may be the push companies need to pivot away from manual protocols. Automation will be essential to keep up with the rate at which large and small molecules can be designed and synthesized with AI.
Conclusion:
Lab automation offers undeniable advantages in terms of precision, throughput, and consistency. Despite these benefits, adoption has been slow due to factors like lack of awareness, high upfront costs, and limited training. However, the rise of AI in drug discovery necessitates a shift towards automated protocols to keep pace with the rapid design and synthesis of molecules. Embracing automation is not just an option, but a necessity for scientists to stay at the forefront of research and make groundbreaking discoveries efficiently and effectively.