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We are proud to announce that our first commercial partnership for Alchemite Engine™ was launched last week. Sign up below to see how Alchemite Engine™ has been integrated with our partner Optibrium’s new product Cerella™, to accelerate drug discovery and increase confidence in lead optimisation.
Join Matt Segall, CEO at Optibrium, and Gareth Conduit, CTO at Intellegens, for the launch of Cerella™, Optibrium’s ground-breaking Augmented Chemistry™ software on the 27th of October at 4 pm (GMT). Cerella™ will be using Intellegens’ Alchemite™ deep learning Engine to bring unprecedented active learning to existent drug discovery data.
Case study with Domino
Virtual experiments allow researchers to use existing empirical data to predict and validate experimental outcomes, and are particularly useful when lab access is limited. The following case study highlights how Domino Printing Services (Domino) used Alchemite™ to leverage their historical data to obtain several novel ink formulations, some of which were later validated via physical experimentation. By utilising virtual experiments in this way, Domino was able to:
- Accelerate the timescale of ink formulation from months to minutes
- Maximise the insights gained from carefully chosen additional experiments
- Reduce the number of physical experiments required
Alchemite™ has new features
The Alchemite™ Analytics platform has numerous data visualization and analytics features to help understand model predictions:
- Material Success Landscape plot. This graph shows the likelihood of designing a formulation that can fulfill the two target properties shown in the x and y axes alongside other targets. This allows engineers to understand and select the best compromise being made between target properties.
- Optimization analytics. This graph shows how the output of an Optimize run varies with one of the input variables. The blue points show the training data that was available to the model, and the red point is the result from the Optimize request, the new successful material, with associated uncertainty. The black line shows how the performance of this material would change if we varied one of the input variables, with the grey region being the corresponding uncertainty.
We are hiring!
We are looking for an outgoing, enthusiastic sales executive with a passion for technology and innovation who will help accelerate our growth and give our customers an outstanding experience.
As a sales executive, you will identify new opportunities and customers with materials, chemicals, and pharmaceutical companies. You will engage with new customers and present the key advantages of our technology and our products.
Do you want to know more about how academic research is translated into industrial applications? Join Alessandro Riccombeni, Precision Medicine Business Development Lead at AWS, and Gareth Conduit, CTO and co-founder at Intellegens, at the Elixir Bioinformatics Industry Forum on the 29th of October at 4.00-5.30 pm GMT.
Pharmacokinetics (PK) describes what happens to a drug after administration. Accurate predictions of PK would enable better decisions regarding the selection of compounds for in vivo studies, reducing the number of experiments required and the associated cost. Join Matt Segall, CEO at Optibrium, Nigel Greene, Director of Data Science and AI at AstraZeneca, and Tom Whitehead, Head of Machine Learning at Intellegens, on the 11th of November at 4 pm (BST).
This project was undertaken in collaboration with Optibrium and AstraZeneca and in this webinar, Tom will describe the successful applications of Alchemite™ for deep learning imputation to the prediction of PK parameters based on sparse in vivo data.