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Using advanced, proprietary deep learning methods to train from incomplete data and correlate noise

Predicting expensive experimental datapoints using minimal known data combined with larger cheaper datasets

Software that can be used online or embeded within your own platforms and workflows

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Use Cases

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Our unique approach

We have developed a unique capability to train and predict models from incomplete data. The technology can be used to link large, easy to acquire, databases with small, hard to acquire datasets. Generated models can be used to design, predict and identify errors.

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Suite of Tools

Our algorithms have been wrapped up into our core Alchemite™ engine, enabling a number of software solutions, allowing easy integration in internal workflows or making use of on-demand cloud compute via fully hosted version.

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Drug Discovery

Given a fragmented dataset the algorithm can learn the underlying correlations to estimate the missing knowledge of how candidate drugs act on proteins and therefore help clients to design new drug cocktails to activate the right proteins to cure disease

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Material Design

The million commercially available materials are characterized by hundreds of properties Our technology learns the underlying correlations to estimate the missing properties and propose a material with the target properties


Intellegens is developing a series of application specific AI modules, designed to address specific, high value, data analysis bottlenecks that we are uncovering through our discussions with existing and new customers.


The first commercially availble product Alchemite™, has been specifically built to work with sparse data and is capable of learning from datasets as little as 0.05% complete.
The algorithm has proven commercial applications in materials design and drug discovery. Trained models can be used for predictions, error detection and parameter optimisation (design).

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Intellegens is a spin-out from the University of Cambridge that has developed a unique Artificial Intelligence (AI) method for training neural networks from incomplete data sets. The technique, developed in the Department of Physics, has been applied in drug discovery and material design but as the technique is generic it can be applied to many domains where there is big, incomplete data.

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Dr Gareth Conduit

Gareth Conduit has a track record of applying artificial intelligence to solve real-world problems, with research contracts held with companies spanning from materials science to healthcare. Gareth holds an academic position at the University of Cambridge and is a Fellow of Gonville & Caius College.

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Ben Pellegrini

Ben is an expert in big data and cloud based platforms who has delivered full-stack, commercial solutions to numerous clients in scientific, retail and health sectors. Ben has worked with several startups, large corporates and public sector clients.

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Dr Tom Whitehead

Tom recently completed his PhD at the University of Cambridge. Tom is leading the application of our novel deep learning approaches to a wide variety of industrial applications, alongside the development of our internal suite of tools and algorithms.


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Machine learning research scientist DEVELOPER

Machine Learning Scientist / Research Engineer (Deep Neural Networks) to join us at the beginning of our journey and work closely with the inventor of the technology. A focus on complex, pioneering methods within Machine Learning such as Markov Models, Gaussian Process, Deep Learning & Reinforcement Learning.

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Full stack developer to work closely with the founding team to develop a platform to deliver the technology. Developing a web based interface to the underlying algorithms, backed by a cloud provider allowing on demand building of neural networks that will be made available via API or downloadable, containerized packages.

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See how our unique AI can solve real world problems