MELLODDY

Machine learning promises to make drug discovery faster, better and cheaper, but it requires access to vast datasets of molecules and their properties. While every pharma company can apply machine learning algorithms to their own data, the true power of this technology comes from combining the (usually ultra-confidential) datasets of several companies to fuel the algorithms.

The MELLODDY project is working on using machine learning to make the most of the combined power of these highly valuable datasets without sharing them, exposing them, or even moving them from where they’re housed.

Because companies’ data is too valuable to risk sharing, MELLODDY is applying a technique called federated learning: this allows datasets to remain behind their firewall, stored independently from each other. With this method, algorithms go back and forth between subsets of each company’s data and the central server, which prevents anyone from knowing which company’s data adds to the central model. This exposes the algorithm to a much wider range of data than any one company has in-house. All this is done while keeping sensitive data safely ensconced within each company’s own infrastructure.

Earlier this year, MELLODDY announced that it had managed to carry out the first successful federated learning run using this new predictive modelling platform.

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