High-throughput chemists like my team and myself usually see the inefficiencies the most. When a bench chemist takes his 8 his LC-MS samples evenly throughout an average day, he tends to quickly get used to the imperfections of the 20 second workflow and stop complaining. But when a high-throughput chemist processes his 96 samples from a well plate at once, small limitations can add up to become a major annoyance or even a disabling inhibitor. . At some point in my career, I was asked to organize his LC-MS data using a specific workflow. This process involved remoting to multiple computers and performing some lengthy workarounds to combine files and export high-throughput data in batches. We could only export analytical data slowly, so if we later noticed new by-products, we would have to weigh the benefits of identifying by-product yield patterns across plates against the value of the time to re-run the export process. was. The benefits were often not immediate, and information was lost regularly. Everyone knows that if you were designing software, you wouldn’t do it this way. But we were chemists, not software designers.

But a year ago, I tried a free introductory coding course. It was run by an acquaintance I knew from my undergraduate days, but as a chemist who was the first to enter the industry, I thought it would be a great idea to do it in parallel with my work. I didn’t think there was much reason to code it, but it seemed like it could come in handy someday.

i was right By the time I was shown a complex LC-MS workflow, my quickly recalled Python knowledge was limited. But the main thing the course taught me was that it can be done. I googled almost every line until I built It wasn’t well written and didn’t take advantage of many useful open source packages that I was unaware of, but it was enough. The new script meant that my team and I were free to capture any information we identified.

Chemists are in a great position to step into both camps

Since venturing into the broad data, coding, and cheminformatics realms of chemical knowledge, I’ve found it to be a great place, albeit an entirely different community than synthetic chemistry. Overall, data chemists are more adamant about the need to open and drive progress than the already open and progressive pharmaceutical synthetic chemistry community. Beyond chemistry, in the world of data, leading a free public course is a badge of honor, so there are broader efforts to distribute learning for free. This allowed me to start learning while working a full-time chemistry job. But when my organic chemist friend and I attended our first chemical data conference, we realized there was a big cultural difference. We found one of the organizers and by that time the beer was worth it and the three of us went to the pub. Very different from a typical late night at a bar after an organic chemistry conference.

A colleague recently told me: But you don’t actually have to “go” anywhere. Chemists are in a great position to step into both camps, as I did in the beginning. I polled some colleagues as to whether they would be interested in taking his beginner’s Python course, and expected the answers to be mostly negative. Instead, about 80% of them wisely said yes. I’m happy because this makes it easy to use time-saving scripts written by programming experts. We had so many questions about how to start coding that we created a web page of resources so we didn’t have to repeat ourselves 100 times.

But now is the time for me to act. He is heavily involved in synthetic chemistry while transitioning to work on data strategy full-time. My passion for chemical data, code, and workflow is not despite being a synthetic organic chemist, but because. Knowledge of chemistry is also an important strength in this role. Many of us have seen digital lab tools that were apparently created without consulting a lab chemist. With a little cooperation, we can change that. My goal is to do great synthetic chemistry faster and with higher quality, so that chemists not only get more out of their experiments, but also free up their time from doing the things they don’t want to do. is to They should work less, not more! Data chemists are making great strides towards their goal of achieving faster, better science, and I can’t wait to be part of it! not.