Automating Chemistry at Light Speed: Drug Discovery with Berkeley Lab

The new automated workflow by Berkeley Lab may revolutionise chemical research, accelerating drug discovery and reaction analysis.

The Lawrence Berkeley National Laboratory (Berkeley Lab) has made some interesting new steps towards automated workflows in chemical research with their development of a real time analysis tool. This new automated workflow, leveraging statistical analysis with nuclear magnetic resonance (NMR) spectroscopy, is set to dramatically improve the way they analyse the outcomes of their chemical reactions. According to Berkeley Lab it promises to accelerate the discovery of new pharmaceutical drugs and the development of novel chemical reactions.

The core advantage of the Berkeley Lab’s automated workflow lies in its ability to rapidly identify the molecular structure of reaction products, even those previously unstudied. This capability could drastically shorten the time required for drug discovery processes and the development of new chemical reactions, potentially leading to significant advancements in medicine and industry. For example, the workflow’s utility in identifying isomers—molecules with the same chemical formula but different atomic arrangements—can significantly benefit synthetic chemistry, especially in pharmaceutical research where time is of the essence.

TOC image reproduced from the Journal of Chemical Information and Modeling

Maxwell C. Venetos, the first author of the study and a former researcher at Berkeley Lab, emphasised the workflow’s significance in real-time reaction analysis. This aspect is particularly exciting for automated chemistry, as it enables researchers to venture into the unknown without being limited by prior knowledge. Moreover, the workflow’s ability to identify isomers—molecules with identical chemical formulas but different atomic arrangements—promises to significantly accelerate synthetic chemistry processes, crucial for pharmaceutical research.

One of the most groundbreaking features of this workflow is its self-generating library capability, allowing users to tune the analysis quality based on their generated data libraries, eliminating dependence on external databases. This is a game-changer, especially in pharmaceutical research, where the speed of identifying potential drug candidates is often hampered by the slow, labor-intensive processes of traditional drug discovery methods.

Traditionally, drug developers have relied on machine-learning algorithms to screen chemical compounds virtually, matching them against potential drug targets. However, when dealing with novel molecules absent from existing databases, researchers face the daunting task of spending days in the lab to determine the molecular makeup of reaction mixtures. The new workflow by the Berkeley Lab team proposes to drastically reduce this time to a couple of hours, leveraging the ability to analyse unpurified reaction mixtures containing multiple compounds quickly and accurately.

The research team, led by senior author Kristin Persson, utilised NMR simulation experiments and sophisticated algorithms, such as the Hamiltonian Monte Carlo Markov Chain (HMCMC), to ensure high statistical accuracy in their analysis. Their work demonstrated the potential of this workflow to accurately identify compound molecules in reaction mixtures and predict their relative concentrations.

Importantly, this automated workflow has been designed as open source, allowing widespread access and usage across both academic and industrial sectors. This democratisation of advanced analytical tools could catalyse a wave of innovation in chemical research, opening doors to new discoveries and applications that were previously unattainable.

The development of this workflow stems from a collaborative effort, highlighting the power of interdisciplinary research. By combining expertise in chemistry, materials science, and computational analysis, the team has laid the groundwork for future advancements in automated laboratory systems capable of analysing millions of new chemical reactions.

The full open-access article “Deconvolution and Analysis of the 1H NMR Spectra of Crude Reaction Mixtures” is available read in the Journal of Chemical Information and Modeling.

Staff Writer

Our in-house science writing team has prepared this content specifically for Lab Horizons

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