Using Deep Learning to Impute Protein Activity
In this webinar, presented by Matt Segall (Optibrium) and Tom Whitehead (Intellegens), you can learn more about Alchemite™, a novel deep learning algorithm. Unlike many deep learning methods, this approach is capable of being trained using sparse and variable input data, typical of those available in drug discovery. This enables Alchemite to learn from correlations between experimental endpoints, as well as between molecular descriptors and protein activities, to more accurately impute the missing activities.
We present a case study that demonstrates that Alchemite outperforms traditional quantitative structure-activity relationship models and discuss how these results can be used to fill in missing data, predict compound activity profiles and identify new active compounds.
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