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, is being applied to drug discovery and material design problems.
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.
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
The Alchemite™ Engine allows for the simple aggregation of csv based data that can be both sparse and noisy, typical …
The machine learning platform for formulation design. Easily experiment, model, and visualise sparse and noisy real-world data.
Ichnite™ is a federated learning platform that helps aggregate models trained on completely separate and private data sources
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