Integrating coding into chemistry helps science progress faster

High-throughput chemists like my team and me generally notice inefficiency the most. If a bench chemist takes maybe eight LC–MS samples, spread evenly through an average day, they quickly get used to a 20-second workflow imperfection and tend not to complain. But when a high-throughput chemist processes 96 samples from a wellplate at once, small limitations accumulate to become a major annoyance or even a disenabling blocker. At one point in my career, I was asked to work with a particular workflow to sort out my LC–MS data. The process involved remoting into multiple computers and performing several lengthy workarounds to combine our files and export high-throughput data in batch. Since we could only export our analytical data slowly, if we noticed a new side-product afterwards, we had to weigh up the benefit of identifying the side-product yield patterns across the plate versus the value of our time rerunning the export process. Since the benefits are often not immediate, information was regularly lost. We could all see that if you were designing software, you wouldn’t do it this way. But we were chemists, not software designers.