Explore the future of lab automation in our latest MestreCast episode, “Automating a Lab – Learning from My Mistakes.” Santi Dominguez engages with John Hollerton, Senior Scientific Director at Mestrelab, who recounts his pioneering work in automating analytical processes within pharmaceutical discovery and development. John reflects on his early endeavors at Glaxo, where his drive to avoid repetitive tasks led him to automate UV spectra analysis, demonstrating that necessity is indeed the mother of invention.

This episode immerses into the critical need for automation in today’s high-throughput analytical labs, emphasizing standardization, process reevaluation, and the significant role of AI in enhancing decision-making and information extraction. Through John’s narrative, you will gain insights into integrating automation effectively, sidestepping common pitfalls, and harnessing technology for substantial progress.

Discover the transformative impact of automation in scientific research, and join us for more enlightening discussions at the intersection of chemistry software, automation, and innovation. Tune in to this episode here or on your preferred platform (Spotify, Applepodcast, iVoox).

Additional Resources

If you liked this episode and would like to read more about the topic we suggest the following articles: Automation for everyone and
No Automation is an Island – Integrating Mgears into your IT infrastructure

If you’re interested in continuing the discussion and exploring how automation can integrate into your environment, don’t hesitate to reach out to us. We’d love to have a chat, understand your needs, and collaborate on finding solutions together.

00:00:00  Intro

00:01:25  How long have you been interested in automation?

00:04:20  Was that very first project a success then?

00:05:45  What drives automation in the analytical lab?

00:08:20  What does analytical automation look like?

00:10:05  What about the last two projects? (information extraction and decision-making)

00:11:50  Is that the limit of what Mestrelab can offer?

00:13:02  How do you go about automating a process?

00:14:25  Is automation inflexible?

00:16:00  Is there anything else that automation enables?

00:17:05  What are the benefits of standardised datasets?

00:19:50  Are there any other sorts of automation not covered so far?

00:21:03  Is storing all that data very expensive?

00:22:25  What about old data that is no longer needed?

00:24:40  What are the challenges when automating systems?

00:29:50  What sort of systems?

00:30:45  How do you get around this?

00:32:44  What’s next for automation?

00:34:05  What do you mean by “high quality datasets”?

00:35:50  When is the big AI change happening?