How Nanopharm uses self-learning models to maximise predictive capabilities & reduces tests in R&D

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As an engineer, you want to use the best technology available to learn from all the data you collect, understand your physical relationships, and ultimately reduce test times. Monolith provides a new AI solution enabling you to build self-learning, intelligent models to reduce the amount of testing to build & validate quality products faster.

This webinar will present how Nanopharm uses Monolith to quickly understand and model complex tests while reducing testing times by using the Monolith platform to optimise their whole R&D process. Additionally, the team is able to predict results and understand relationships and sensitivities between input parameters and their component/product performance, leveraging their past test experience and datasets.

Our guest speaker Dr. Will Ganley, Head of Computational Pharmaceutics, will show how he and his engineering team at Nanopharm use self-learning models to develop a data processing and modelling method. They were able to design workflows that facilitated modelling and optimisation of the required size/shape distributions, cut testing times from two days to less than two minutes for a single batch using performant customer-specific models.

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Monolith enables engineers all over the world to:

• Understand physically intractable problems
• Fully explore multiple virtual test scenarios
• Reduce costs and time investment throughout the whole R&D cycle
• Increase confidence in predictions & recommendations on which tests to run next