Credit: Jenny Nuss/Berkeley Lab
Lawrence Berkeley National Laboratory (Berkeley Lab) researchers have developed a novel workflow to allow researchers to analyze their reactions in real-time. The method could provide a faster route for discovering new targets for pharmaceuticals and accelerate the discovery of new chemical reactions.
The workflow, published in the Journal of Chemical Information and Modeling, relies on the use of statistical analysis to automate processing data resulting from nuclear magnetic resonance (NMR) spectroscopy. The workflow can rapidly identify the molecular structure of products resulting from chemical reactions, even those that have never been studied before.
“What excites people the most about this technique is its potential for real-time reaction analysis, which is an integral part of automated chemistry,” said Maxwell C. Venetos, a former researcher in Berkeley Lab’s Materials Sciences Division. “Our workflow really allows you to start pursuing the unknown. You are no longer constrained by things that you already know the answer to.”
The workflow is poised to be especially helpful with drug discovery research. Currently, drug developers utilize machine learning to comb through libraries of known compounds to identify potential drug targets. While this method automates discoveries of known compounds if the molecule is so new that its structure remains unknown, researchers must then spend days in the lab to sort out its molecular makeup.
“But with our new workflow, you could feasibly do all of that work within a couple of hours,” said Venetos. The time savings are a result of the workflow's ability to accurately analyze the NMR spectra of unpurified reaction mixtures containing multiple compounds, a process that is not possible with conventional analysis methods.
“I’m very excited about this work as it applies novel data-driven methods to the age-old problem of accelerating synthesis and characterization,” said Kristin Persson, a faculty senior scientist in Berkeley Lab’s Materials Sciences Division and UC Berkeley professor of materials science and engineering.
Now that the team has demonstrated the workflow potential, they hope to incorporate it within an automated laboratory to analyze the NMR spectra of thousands of new reactions simultaneously.