Protein - protein interacnullons (PPIs) are associated with various diseases, including cancer, infecnullons, and neurodegeneranullve disorders. Obtaining three - dimensional structural informanullon on these PPIs serves as a foundanullon to interfere with those or to guid e drug design. Various strategies can be followed to model those complexes, all typically resulnullng in a large number of models. A challenging st e p in this process is the idennullfica-nullon of good models ( near - nanullve PPI conformanullons) from the large pool of generated models . T o address this challenge, we previously developed DeepRank - GNN - esm, a graph - based deep learning algorithm for ranking modelled PP I structures harnessing the power of protein language model s .

Google is revealing new details about the making of its DeepRank algorithm which surfaces more relevant search results by understanding language the way humans do. DeepRank is discussed in length in a brand new video from Google about how search works. Among other aspects of search, Google's video goes over the development, testing, and approval process that each algorithm update goes through. DeepRank was launched in 2019 as BERT, and is named for the deep learning methods used by BERT and the ranking aspect of search. Think of DeepRank as the integration of BERT into Google Search.