A Global Deep Learning Model for Global Health Drug Discovery

This webinar was presented by Samar Mahmoud and Matt Segall. 

In this webinar, we described the generation and validation of a ‘global’ model using deep learning imputation on a data set of 300,000 compounds and 500 experimental endpoints, targeting global health indications.

We demonstrated how this global model can be applied to individual optimisation projects, offering improved compounds design performance over ‘local’ project-specific models by learning across a broad chemical diversity.

Even including unrelated endpoints from many projects does not cause a loss of performance for individual projects and endpoints – an example of “build once, run everywhere.”

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