AstraZeneca chooses DeepMatter for optimisation of compound synthesis


DeepMatter has joined forces with AstraZeneca to improve productivity using digital equipment enabled with machine learning and AI

Photo as seen on company website

Photo as seen on company website

UK-based DeepMatter has announced a collaboration with AstraZeneca to use digital technologies to improve the productivity and reproducibility of compound synthesis. Scientists from the two organisations will work together to improve the productivity of synthesising single compounds and compound libraries based on unique, structured data from the DigitalGlassware technology.

The conditions of a reaction, such as temperature, solvent and catalysts, are important to the success of any experiment. DigitalGlassware captures and analyses information about the chemical reaction. A multi-sensor probe sits inside the reaction vessel, providing real-time data (temperature, pressure, UV light levels and more) while an environmental sensor records ambient conditions.

Data from external laboratory hardware can also be recorded through software application programming interfaces. The structured data is collected and stored in the cloud alongside each process carried out during the reaction, contextualising the actions of the user in the lab.

Displayed in real time, the data can be interrogated using multiple views, enabling the analysis of reaction runs and the re-playing of syntheses. By capturing in situ chemical data alongside the experimental intent, observations and outcomes, it is expected that machine learning and AI algorithms could yield cost and time savings whilst also providing novel insights into chemistry.

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Mark Warne, CEO of DeepMatter, said: “We’ve been impressed with the automated chemistry platforms developed at AstraZeneca sites for autonomous delivery of new lead series. We see an opportunity to draw together knowledge from the DigitalGlassware platform to enable machine learning and AI technologies to increase the certainty of producing a high quality and choice of candidate drug molecules.”