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Here, we used #MachineLearning to design high diversity antibody variants with orders of magnitude greater potency than could be found with conventional directed mutagenesis. #ProteinML #AntibodyEngineering
doi.org/10.1038/s41467… @NatureComms 1/4
doi.org/10.1038/s41467… @NatureComms 1/4
Our method uses protein embeddings and Bayesian ML to design optimized antibody variant libraries, and we compare directly with other methods in a head-to-head prospective design study. 2/4
It was wonderful to collaborate with Lin Li, Rajmonda Caceres, Matt Walsh, and the rest of the @MITLL, @MIT, and @AAlphaBio teams on this project! 3/4
If you want to try out these kinds of ML methods in your own work with a user-friendly web interface, check out OpenProtein.AI and sign up for the beta at forms.gle/UeD9wDLvdG9LRw…! 4/4
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